A reasonably recent version of my CV can be downloaded here [pdf].

A more up to date list of publications can be found on Google Scholar.

List of Publications, Dr Bernhard Kainz

In Computer Science peer reviewed full papers at leading, top-ranked conferences are as important and sometimes more selective as journal publications. See e.g.,http://bit.ly/2KzvvyZ or http://bit.ly/2ptl1tF for a discussion of this topic.

My research about human centred AI in health care is at the interface of Computer Science, Medical Image Analysis, Machine Learning and Clinical Science. Thus, both, journal and conference publications count equally much, and I have a good record in both categories.

The leading journals in my area are IEEE Transactions on Medical Imaging (IEEE Trans Med Imag), Elsevier Medial Image Analysis (Med Image Anal) and from a machine learning perspective the Journal of Machine Learning Research (JMLR). The leading conference is the international Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) and its associated workshops.

Currently, machine learning related research is done at an extremely fast pace. Like in Physics and Maths, preprints, e.g. on arXiv.org that have not been fully peer reviewed are important in our field. For example, according to google scholar, two of my most cited papers are preprints that have only been published later as fully peer reviewed journal publications. Interestingly, authors predominantly post works on arXiv that are of high enough quality to likely pass peer-review. Most of these papers are accepted after peer review at a later point.

Publicly available online preprints and source code repositories foster open science and are becoming more and more important in Computer Science especially in Machine Learning related domains; thus, these contributions are listed as well.

Due to my proximity to the healthcare sector and bioengineering domain I also co-author publications in clinical top journals such as The Lancet and bioengineering journals like the Elsevier Journal of Biomechanics (J Biomech).

Besides traditional publication formats, challenges (public competitions) are a common way in medical image analysis to benchmark algorithms against each other. Winning one of these competitions is highly prestigious.

There are no strict rules for author positions in my area of Computer Science. Significant contribution to the content of the paper is a must for co-authorship. Usually, Ph.D. students are preferred for the first author position and student/post-doctoral contributors follow. Principal investigators share the last author positions, sorted by their institutional supervision relationship to the first author and seniority. I have contributed in all of these roles as evident in the (automatically generated) list below.

List of Publications

Contributions from 2020/21 to date

Peer reviewed Q1 (scimagojr.com) journal papers:

  1. Cao, Y., Monod, A., Vlontzos., A, Schmidtke., L, Kainz, B. Topological information retrieval with dilation-invariant bottleneck comparative measures. Information and Inference: A Journal of the IMA. 2023 Sep;12(3):iaad022.

  2. Sarapata G, Dushin Y, Morinan G, Ong J, Budhdeo S, Kainz B, O’Keeffe J. Video-based activity recognition for automated motor assessment of Parkinson’s disease. IEEE Journal of Biomedical and Health Informatics. 2023 Jul 25.

  3. Hinterreiter A, Humer C, Kainz B, Streit M. ParaDime: A Framework for Parametric Dimensionality Reduction. InComputer Graphics Forum 2023 Jun (Vol. 42, No. 3, pp. 337-348).

  4. Day, T.G., Matthew, J., Budd, S., Hajnal, J.V., Simpson, J.M., Razavi, R., Kainz, B., Sonographer interaction with artificial intelligence: collaboration or conflict?. Ultrasound in Obstetrics & Gynecology.

  5. Day, T. G., Simpson, J. M., Razavi, R., & Kainz, B. (2023). Improving image labelling quality. Nature Machine Intelligence, 5(4), 335-336.

  6. Wright, R., Gomez, A., Zimmer, V.A., Toussaint, N., Khanal, B., Matthew, J., Skelton, E., Kainz, B., Rueckert, D., Hajnal, J.V. and Schnabel, J.A., 2023. Fast fetal head compounding from multi-view 3D ultrasound. Medical Image Analysis, p.102793.

  7. Avgerinos, E., Oppenheimer, J., Al-Noor, F., Karimaghaei, R., Adler, A., Singöhl, S., Kainz, B., Mandegaran, R., Heinrich, M., Spiliopoulos, S. and Geroulakos, G., 2023. Remote Expert Deep Venous Thrombosis Triaging of Novice-User Compression Sonography with Artificial Intelligence Guidance. Journal of Vascular Surgery: Venous and Lymphatic Disorders, 11(2), p.449.

  8. Day, T.G., Kainz, B., Razavi, R. and Simpson, J., 2023. RE: Wang et al. Diagnosis of fetal total anomalous pulmonary venous connection based on the post‐left atrium space ratio using artificial intelligence. Prenatal Diagnosis, 43(3), pp.400-401.

  9. Vlontzos, A., Kainz, B. and Gilligan-Lee, C.M., 2023. Estimating categorical counterfactuals via deep twin networks. Nature Machine Intelligence, 5(2), pp.159-168.

  10. Zimmer, V.A., Gomez, A., Skelton, E., Wright, R., Wheeler, G., Deng, S., Ghavami, N., Lloyd, K., Matthew, J., Kainz, B. and Rueckert, D., 2023. Placenta segmentation in ultrasound imaging: Addressing sources of uncertainty and limited field-of-view. Medical Image Analysis, 83, p.102639.

  11. Ma, Q., Li, L., Robinson, E.C., Kainz, B., Rueckert, D. and Alansary, A., 2022. CortexODE: Learning Cortical Surface Reconstruction by Neural ODEs. IEEE Transactions on Medical Imaging.

  12. Zimmerer, D., Full, P.M., Isensee, F., Jäger, P., Adler, T., Petersen, J., Köhler, G., Ross, T., Reinke, A., Kascenas, A., Jensen, B.S., O’Neil, A.Q., Tan, J., Hou, B., Batten, J., Qiu, H., Kainz, B., and others 2022. MOOD 2020: A public Benchmark for Out-of-Distribution Detection and Localization on medical Images. IEEE Transactions on Medical Imaging, 41(10), pp.2728-2738.

  13. Liu, T., Meng, Q., Huang, J.J., Vlontzos, A., Rueckert, D. and Kainz, B., 2022. Video summarization through reinforcement learning with a 3D spatio-temporal u-net. IEEE Transactions on Image Processing, 31, pp.1573-1586.

  14. Dou, Q., So, T.Y., Jiang, M., Liu, Q., Vardhanabhuti, V., Kaissis, G., Li, Z., Si, W., Lee, H.H., Yu, K. and Feng, Z., Dong, L., Burian, E., Jungmann, F., Braren, R., Makowski, M., Kainz, B., Rueckert, D., Glocker, B, Yu, SCH, Heng, PA, 2021. Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study. NPJ digital medicine, 4(1), p.60.

  15. Matthew J, Skelton E, Day TG, Zimmer VA, Gomez A, Wheeler G, Toussaint N, Liu T, Budd S, Lloyd K, Wright R. Exploring a new paradigm for the fetal anomaly ultrasound scan: Artificial intelligence in real time. Prenatal diagnosis. 2022 Jan;42(1):49-59.

  16. Kainz, B., Heinrich, M.P., Makropoulos, A., Oppenheimer, J., Mandegaran, R., Sankar, S., Deane, C., Mischkewitz, S., Al-Noor, F., Rawdin, A.C. and Ruttloff, A., 2021. Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning. NPJ Digital Medicine, 4(1), p.137.

  17. Budd, S., Robinson, E.C. and Kainz, B., 2021. A survey on active learning and human-in-the-loop deep learning for medical image analysis. Medical Image Analysis, 71, p.102062.

  18. Day, T.G., Kainz, B., Hajnal, J., Razavi, R. and Simpson, J.M., 2021. Artificial intelligence, fetal echocardiography, and congenital heart disease. Prenatal Diagnosis, 41(6), pp.733-742.

Other peer reviewed journals:

  1. Gomez, A., Zimmer, V.A., Wheeler, G., Toussaint, N., Deng, S., Wright, R., Skelton, E., Matthew, J., Kainz, B., Hajnal, J. and Schnabel, J., 2022. PRETUS: A plug-in based platform for real-time ultrasound imaging research. SoftwareX, 17, p.100959.

  2. Vlontzos, A., Rueckert, D., Kainz, B., A Review of Causality for Learning Algorithms in Medical Image Analysis. Melba Journal Volume 1 November 2022 issue 2022:028

  3. Tan, J., Hou, B., Batten, J., Qui, H., Kainz, B., Detecting Outliers with Foreign Patch Interpolation, Melba Journal Volume 1 April 2022 issue 2022:013

  4. Grzech, D., Azampour, MF, Qiu, H., Glocker, B., Kainz, B., Le Folgoc, L., Uncertainty quantification in non-rigid image registration via stochastic gradient Markov chain Monte Carlo, Melba Journal Volume 1 UNSURE2020 special issue 2021:016

  5. Chotzoglou, E., Day, T., Tan, J., Matthew, J., Lloyd, D., Razavi, R., Simpson, J., Kainz, B., Learning normal appearance for fetal anomaly screening: Application to the unsupervised detection of Hypoplastic Left Heart Syndrome. Melba Journal Volume 1 September 2021 issue 2021:012

Peer reviewed full papers at scientific conferences:

  1. Cechnicka, S., Ball, J., Reynaud, H., Arthurs, C., Roufosse, C., Kainz, B., Realistic Data Enrichment for Robust Image Segmentation in Kidney Transplant Pathology, MICCAI Domain Adaptation and Representation Transfer 2023

  2. Müller, J., Baugh, M., Tan, J., Dombrowski, M., Kainz, B., Confidence-Aware and Self-Supervised Image Anomaly Localisation. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging at MICCAI 2023

  3. Basaran, B., Zhang, W., Qiao, M., Kainz, B., Matthews, P.M., Bai, W., LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentation, 3rd MICCAI Workshop on Data Augmentation, Labeling, and Imperfections

  4. Shkëmbi, G., Müller, J., Li, Z., Breininger, K., Schüffler, P., Kainz, B., Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis, Data Engineering in Medical Imaging Workshop at MICCAI 2023

  5. Jehn, C., Müller, J., Kainz, B., Learnable Slice-to-Volume Reconstruction for Motion Compensation in fetal Magnetic Resonance Imaging, German Workshop on Medical Image Computing - BVM 2023

  6. Dombrowski, M., Reynaud, H., Baugh, M., Kainz, B., Foreground-Background Separation through Concept Distillation from Generative Image Foundation Models, In International Conference on Computer Vision (ICCV’23), (acceptance rate  25%)

  7. Li, L., Ma, Q., Ouyang, Ch., Li, Z., Meng, Q., Zhang, W., Qiao, M., Kyriakopoulou, V., Hajnal, J.V., Rueckert, D., Kainz B., Robust Segmentation via Topology Violation Detection and Feature Synthesis, In Medical Image Computing and Computer Assisted Intervention–MICCAI 2023: 26th International Conference, Vancouver, Canada, to appear, 2023. Springer, 2023. (acceptance rate  25%)

  8. Zhang, W., Basaran, B., Meng, Q., Baugh, M., Stelter, J., Lung, P., Patel, U., Bai, W., Karampinos, D., Kainz, B., MoCoSR: Respiratory Motion Correction and Super-Resolution for 3D Abdominal MRI, In Medical Image Computing and Computer Assisted Intervention–MICCAI 2023: 26th International Conference, Vancouver, Canada, to appear, 2023. Springer, 2023. (acceptance rate  25%)

  9. Ma, Q., Li, L., Kyriakopoulou, V., Hajnal, J.V., Robinson, E., Kainz, B., Rueckert, D., Conditional Temporal Attention Networks for Neonatal Cortical Surface Reconstruction, In Medical Image Computing and Computer Assisted Intervention–MICCAI 2023: 26th International Conference, Vancouver, Canada, to appear, 2023. Springer, 2023. (acceptance rate  25%)

  10. Baugh, M., Tan, J., Müller, J., Dombrowski, M., Batten, J., and Kainz, B., Many tasks make light work: Learning to localise medical anomalies from multiple synthetic tasks. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2023: 26th International Conference, Vancouver, Canada, to appear, 2023. Springer, 2023. (early accept rate < 10%)

  11. Reynaud, H., Qiao, M., Dombrowski, M., Day, T., Razavi, R., Gomez, A., Leeson, P., and Kainz, B., Feature-conditioned cascaded video diffusion models for precise echocardiogram synthesis. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2023: 26th International Conference, Vancouver, Canada, to appear, 2023. Springer, 2023 (early accept rate < 10%)

  12. Schmidtke, L., Hou, B., Vlontzos, A. and Kainz, B., 2023, February. Self-supervised 3D Human Pose Estimation in Static Video via Neural Rendering. In Computer Vision–ECCV 2022 Workshops: Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part III (pp. 704-713). Cham: Springer Nature Switzerland.

  13. Schlüter, H.M., Tan, J., Hou, B. and Kainz, B., 2022, October. Natural synthetic anomalies for self-supervised anomaly detection and localization. In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXI (pp. 474-489). Cham: Springer Nature Switzerland. (accetance rate  28%)

  14. Tan, J., Kart, T., Hou, B., Batten, J. and Kainz, B., 2022. Metadetector: Detecting outliers by learning to learn from self-supervision. In Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis: MICCAI 2021 Challenges: MIDOG 2021, MOOD 2021, and Learn2Reg 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27–October 1, 2021, Proceedings (pp. 119-126). Cham: Springer International Publishing

  15. Li, L., Ma, Q., Li, Z., Ouyang, C., Zhang, W., Price, A., Kyriakopoulou, V., Grande, L.C., Makropoulos, A., Hajnal, J. and Rueckert, D., 2022, December. Fetal Cortex Segmentation with Topology and Thickness Loss Constraints. In Ethical and Philosophical Issues in Medical Imaging, Multimodal Learning and Fusion Across Scales for Clinical Decision Support, and Topological Data Analysis for Biomedical Imaging: 1st International Workshop, EPIMI 2022, 12th International Workshop, ML-CDS 2022, 2nd International Workshop, TDA4BiomedicalImaging, Held in Conjunction with MICCAI 2022, Singapore, September 18–22, 2022, Proceedings (pp. 123-133). Cham: Springer Nature Switzerland.

  16. Reynaud, H., Vlontzos, A., Dombrowski, M., Gilligan Lee, C., Beqiri, A., Leeson, P. and Kainz, B., 2022, September. D’artagnan: Counterfactual video generation. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part VIII (pp. 599-609). Cham: Springer Nature Switzerland. (accetance rate  30%)

  17. Lebbos, C., Barcroft, J., Tan, J., Müller, J., Baugh, M., Vlontzos, A., Saso, S. and Kainz, B., 2022, September. Adnexal Mass Segmentation with Ultrasound Data Synthesis. In Simplifying Medical Ultrasound: Third International Workshop, ASMUS 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings (pp. 106-116). Cham: Springer International Publishing.

  18. Baugh, M., Tan, J., Vlontzos, A., Müller, J.P. and Kainz, B., 2022, September. nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods. In Uncertainty for Safe Utilization of Machine Learning in Medical Imaging: 4th International Workshop, UNSURE 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings (pp. 103-112). Cham: Springer Nature Switzerland.

  19. Ouyang, C., Wang, S., Chen, C., Li, Z., Bai, W., Kainz, B. and Rueckert, D., 2022, September. Improved post-hoc probability calibration for out-of-domain MRI segmentation. In Uncertainty for Safe Utilization of Machine Learning in Medical Imaging: 4th International Workshop, UNSURE 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings (pp. 59-69). Cham: Springer Nature Switzerland.

  20. Reinke, A., Maier-Hein, L., Christodoulou, E., Glocker, B., Scholz, P., Isensee, F., Kleesiek, J., Kozubek, M., Reyes, M., Riegler, M.A. and Wiesenfarth, M., 2022. Metrics Reloaded-A new recommendation framework for biomedical image analysis validation. In Medical Imaging with Deep Learning.

  21. Grzech, D., Azampour, M.F., Glocker, B., Schnabel, J., Navab, N., Kainz, B. and Le Folgoc, L., 2022. A variational Bayesian method for similarity learning in non-rigid image registration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 119-128). (acceptance rate  25%)

  22. Li, L., Sinclair, M., Makropoulos, A., Hajnal, J.V., David Edwards, A., Kainz, B., Rueckert, D. and Alansary, A., 2021. CAS-Net: conditional atlas generation and brain segmentation for fetal MRI. In Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis: 3rd International Workshop, UNSURE 2021, and 6th International Workshop, PIPPI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings 3 (pp. 221-230). Springer International Publishing.

  23. Ma, Q., Robinson, E.C., Kainz, B., Rueckert, D. and Alansary, A., 2021. PialNN: a fast deep learning framework for cortical pial surface reconstruction. In Machine Learning in Clinical Neuroimaging: 4th International Workshop, MLCN 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings 4 (pp. 73-81). Springer International Publishing.

  24. Chartsias, A., Gao, S., Mumith, A., Oliveira, J., Bhatia, K., Kainz, B. and Beqiri, A., 2021. Contrastive learning for view classification of echocardiograms. In Simplifying Medical Ultrasound: Second International Workshop, ASMUS 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings 2 (pp. 149-158). Springer International Publishing.

  25. Budd, S., Day, T., Simpson, J., Lloyd, K., Matthew, J., Skelton, E., Razavi, R. and Kainz, B., 2021. Can non-specialists provide high quality gold standard labels in challenging modalities? In Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health: Third MICCAI Workshop, DART 2021, and First MICCAI Workshop, FAIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27 and October 1, 2021, Proceedings 3 (pp. 251-262). Springer International Publishing.

  26. Tan, J., Hou, B., Day, T., Simpson, J., Rueckert, D. and Kainz, B., 2021. Detecting outliers with poisson image interpolation. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part V 24 (pp. 581-591). Springer International Publishing. (accetance rate  30%)

  27. Budd, S., Sinclair, M., Day, T., Vlontzos, A., Tan, J., Liu, T., Matthew, J., Skelton, E., Simpson, J., Razavi, R. and Glocker, B., 2021. Detecting hypo-plastic left heart syndrome in fetal ultrasound via disease-specific atlas maps. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VII 24 (pp. 207-217). Springer International Publishing.

  28. Reynaud, H., Vlontzos, A., Hou, B., Beqiri, A., Leeson, P. and Kainz, B., 2021. Ultrasound video transformers for cardiac ejection fraction estimation. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VI 24 (pp. 495-505). Springer International Publishing.

  29. Hou, B., Kaissis, G., Summers, R.M. and Kainz, B., 2021. Ratchet: Medical transformer for chest x-ray diagnosis and reporting. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VII 24 (pp. 293-303). Springer International Publishing. (accetance rate  30%)

  30. Schmidtke, L., Vlontzos, A., Ellershaw, S., Lukens, A., Arichi, T. and Kainz, B., 2021. Unsupervised human pose estimation through transforming shape templates. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2484-2494). (accetance rate  25%)

Peer reviewed conference abstracts:

  1. Son, J.H., Alansary, A., Rueckert, D., Kainz, B. and Hou, B., 2021. Synthesis of diabetic retina fundus images using semantic label generation. In Medical Imaging with Deep Learning.

Public source code and demos:

  1. UVT: https://github.com/HReynaud/UVT

  2. dartagnan: https://github.com/HReynaud/dartagnan

  3. EchoDiffusion: https://github.com/HReynaud/EchoDiffusion Demo: https://huggingface.co/spaces/HReynaud/EchoDiffusionDemo

  4. PialNN: https://github.com/m-qiang/PialNN,

  5. CortexODE: https://github.com/m-qiang/CortexODE.

  6. Twin Causal Nets: https://github.com/thanosvlo/Twin_Causal_Nets

  7. MARL-for-Anatomical-Landmark-Detection:

    https://github.com/thanosvlo/MARL-for-Anatomical-Landmark-Detection

  8. Causal-Future-Prediction-in-a-Minkowski-Space-Time:

    https://github.com/thanosvlo/Causal-Future-Prediction-in-a-Minkowski-Space-Time

  9. ShaderLabWeb: https://github.com/bkainz/ShaderLabWeb

  10. nnOOD: https://github.com/matt-baugh/nnOOD

Preprints:

  1. Cechnicka, S., Ball, J., Arthurs, C., Roufosse, C. and Kainz, B., 2023. Realistic Data Enrichment for Robust Image Segmentation in Histopathology. arXiv preprint arXiv:2304.09534.

  2. Dombrowski, M., Reynaud, H., Müller, J.P., Baugh, M. and Kainz, B., 2023. Pay Attention: Accuracy Versus Interpretability Trade-off in Fine-tuned Diffusion Models. arXiv preprint arXiv:2303.17908.

  3. Müller, J.P., Baugh, M., Tan, J., Dombrowski, M. and Kainz, B., 2023. Confidence-Aware and Self-Supervised Image Anomaly Localisation. arXiv preprint arXiv:2303.13227.

  4. Reynaud, H., Qiao, M., Dombrowski, M., Day, T., Razavi, R., Gomez, A., Leeson, P. and Kainz, B., 2023. Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis. arXiv preprint arXiv:2303.12644.

  5. Reinke, A., Tizabi, M.D., Baumgartner, M., Eisenmann, M., Heckmann-Nötzel, D., Kavur, A.E., Rädsch, T., Sudre, C.H., Acion, L., Antonelli, M. and Arbel, T., 2023. Understanding metric-related pitfalls in image analysis validation. ArXiv.

  6. Dombrowski, M., Reynaud, H., Baugh, M. and Kainz, B., 2022. Zero-Shot Object Segmentation through Concept Distillation from Generative Image Foundation Models. arXiv preprint arXiv:2212.14306.

  7. Sarapata, G., Morinan, G., Dushin, Y., Kainz, B., Ong, J. and O’Keeffe, J., 2022. Video-based activity recognition for automated motor assessment of Parkinson’s disease.

  8. Hinterreiter, A., Humer, C., Kainz, B. and Streit, M., 2022. ParaDime: A Framework for Parametric Dimensionality Reduction. arXiv preprint arXiv:2210.04582.

  9. Maier-Hein, L. and Menze, B., … 2022. Metrics reloaded: Pitfalls and recommendations for image analysis validation. arXiv. org, (2206.01653).

  10. Vlontzos, A., Rueckert, D. and Kainz, B., 2022. A review of causality for learning algorithms in medical image analysis. arXiv preprint arXiv:2206.05498.

  11. Vlontzos, A., Reynaud, H. and Kainz, B., 2022. Is more data all you need? a causal exploration. arXiv preprint arXiv:2206.02409.

  12. Kainz, B., Makropoulos, A., Oppenheimer, J., Deane, C., Mischkewitz, S., Al-Noor, F., Rawdin, A.C., Stevenson, M.D., Mandegaran, R., Heinrich, M.P. and Curry, N., 2021. Non-invasive Diagnosis of Deep Vein Thrombosis from Ultrasound with Machine Learning. medRxiv, pp.2021-01.

  13. Vlontzos, A., Kainz, B. and Gilligan-Lee, C.M., 2021. Estimating the probabilities of causation via deep monotonic twin networks. arXiv preprint arXiv:2109.01904, pp.1-10.

  14. Vlontzos A, Cao Y, Schmidtke L, Kainz B, Monod A. Topological data analysis of database representations for information retrieval. arXiv preprint arXiv:2104.01672. 2021.

  15. Chotzoglou, E., Day, T., Tan, J., Matthew, J., Lloyd, D., Razavi, R., Simpson, J. and Kainz, B., 2020. Learning normal appearance for fetal anomaly screening: Application to the unsupervised detection of Hypoplastic Left Heart Syndrome. arXiv preprint arXiv:2012.03679.

  16. Tan, J., Hou, B., Batten, J., Qiu, H. and Kainz, B., 2020. Detecting outliers with foreign patch interpolation. arXiv preprint arXiv:2011.04197.

Patents:

  1. Blood vessel obstruction diagnosis method, apparatus and system F Al-Noor, S Mischkewitz, A Makropoulos, R Tanno, B Kainz, O Oktay US Patent 11,464,477

  2. Method and system for confidence estimation of a trained deep learning modelA Makropoulos, B Kainz US Patent App. 17/619,202

Contributions from 2018 to 2020/21:

Peer reviewed Q1 (scimagojr.com) journal papers:

  1. Meng, Q., Matthew, J., Zimmer, V.A., Gomez, A., Lloyd, D.F.A., Rueckert, D., Kainz, B., “Mutual Information-based Disentangled Neural Networks for Classifying Unseen Categories in Different Domains: Application to Fetal Ultrasound Imaging.” IEEE Trans Med Imag 2020

  2. Miolane, N., Guigui, N., Le Brigant, A., Mathe, J., Hou, B., Thanwerdas, Y., Heyder, St., Peltre, O., Koep, N., Zaatiti, H., Hajri, H., Cabanes, Y., Gerald, Th., Chauchat, P., Shewmake, Ch., Brooks, D., Donnat, C., Kainz, B., Pennec, X., "Geomstats: A Python Package for Riemannian Geometry in Machine Learning", Editors: Francis Bach, David Blei, and Bernhard Schölkopf, To appear in Journal of Machine Learning Research (JMLR) 2020

  3. Jiang, G., Kainz, B., “Deep Radiance Caching: Convolutional Autoencoders Deeper in Ray Tracing”. Computers & Graphics. 2020 Oct 7. Volume 94, February 2021, Pages 22-31 (reproducibility stamp award)

  4. Robinson, R., Valindria, V.V., Bai, W., Oktay, O., Kainz, B., Suzuki, H., Sanghvi, M.M., Aung, N., Paiva, J.M., Zemrak, F., Fung, K., Lukaschuk, E., Lee, A.M., Carapella, V., Kim, Y.J., Piechnik, St.K., Neubauer, St., Petersen, St.E., Page, Ch., Matthews, P.M., Rueckert, D., Glocker, B., “Automated quality control in image segmentation: application to the UK Biobank cardiovascular magnetic resonance imaging study.” Journal of Cardiovascular Magnetic Resonance. 2019 Dec 1;21(1):18.

  5. Matthew, J., Deprez, M., Uus, A., Holder, M., McCabe, L., Van Poppel, M., Skelton, E., Smith, S., Sankaran, S., Wright, R., Patkee, P.A., Kainz, B., Hajnal, J., Rutherford, M., “Syndromic craniofacial dysmorphic feature assessment in utero: potential for a novel imaging methodology with reconstructed 3D fetal MRI models.” Ultrasound in Obstetrics & Gynecology. 2019 Oct;54:29-.

  6. Meng, Q., Sinclair, M., Zimmer, V., Hou, B., Rajchl, M., Toussaint, N., Oktay, O., Schlemper, J., Gomez ,A., Housden, J., Matthew, J., Rueckert, D., Schnabel, J., Kainz, B., “Weakly supervised estimation of shadow confidence maps in fetal ultrasound imaging.” IEEE Trans Med Imag. 2019 Apr 25;38(12):2755-67.

  7. Lloyd, D.F.A., Pushparajah, K., Simpson, J.M., Van Amerom, J.F., Van Poppel, M.P., Schulz, A., Kainz, B., Deprez, M., Lohezic, M., Allsop, J., Mathur, S., Bellsham-Revell, H., Vigneswaran, T., Charakida, M., Miller, O., Zidere, V., Sharland, G., Rutherford, M., Hajnal, J.V., Razavi, R., “Three-dimensional visualisation of the fetal heart using prenatal MRI with motion-corrected slice-volume registration: a prospective, single-centre cohort study.” The Lancet. 2019 Apr 20;393(10181):1619-27.

  8. Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., Rueckert, D., “Attention gated networks: Learning to leverage salient regions in medical images.” Medical image analysis. 2019 Apr 1;53:197-207.

  9. Alansary, A., Oktay, O., Li, Y., Le Folgoc, L., Hou, B., Vaillant, G., Kamnitsas, K., Vlontzos, A., Glocker, B., Kainz, B., Rueckert, D., “Evaluating reinforcement learning agents for anatomical landmark detection.” Medical image analysis. 2019 Apr 1;53:156-64.

  10. Castro, D.C., Tan, J., Kainz, B., Konukoglu, E., Glocker, B., “Morpho-Mnist: Quantitative assessment and diagnostics for representation learning.” Journal of Machine Learning Research. 2019;20(178):1-29.

  11. Bai, W., Sinclair, M., Tarroni, G., Oktay, O., Rajchl, M., Vaillant, G., Lee, A.M., Aung, N., Lukaschuk, E., Sanghvi, M. M., Zemrak, F., Fung, K., Paiva, J.M., Carapella, V., Kim, Y.J., Suzuki, H., Kainz, B., Matthews, P.M., Petersen, St. E., Piechnik, St. K., Neubauer, St., Glocker, B., Rueckert, D., “Automated cardiovascular magnetic resonance image analysis with fully convolutional networks”. Journal of Cardiovascular Magnetic Resonance. 2018 Dec 1;20(1):65.

Other peer reviewed journals:

  1. Skelton, E., Matthew, J., Li, Y., Khanal, B., Martinez, J.C., Toussaint, N., Gupta, C., Knight, C., Kainz, B., Hajnal, J.V. and Rutherford, M., 2021. Towards automated extraction of 2D standard fetal head planes from 3D ultrasound acquisitions: A clinical evaluation and quality assessment comparison. Radiography, 27(2), pp.519-526.

Peer reviewed full papers at scientific conferences:

  1. Hinterreiter, A., Streit, M., Kainz, B., “Projective Latent Interventions for Understanding and Fine-Tuning Classifiers”. In Interpretable and Annotation-Efficient Learning for Medical Image Computing 2020 at MICCAI 2020 Oct 4 (pp. 13-22), Springer, Cham. (best paper award)

  2. Budd, S., Patkee, P., Baburamani, A., Rutherford, M., Robinson, E.C., Kainz, B., “Surface Agnostic Metrics for Cortical Volume Segmentation and Regression.” In 3rd international workshop on machine learning in clinical neuroimaging (MLCN2020) at MICCAI 2020, Springer, Cham. (best paper honourable mention award)

  3. Tan, J., Hou, B., Batten, J., Qiu, H., Kainz, B., “Detecting Outliers with Foreign Patch Interpolation” Medical Out-of-Distribution Analysis Challenge, Melba Journal, Volume 1, April 2022 issue, 2022:013, http://medicalood.dkfz.de/web/ https://arxiv.org/abs/2011.04197 (winning entry)

  4. Liu, T., Meng, Q., Vlontzos, A., Tan, J., Rueckert, D., Kainz, B., "Ultrasound Video Summarization Using Deep Reinforcement Learning", In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 pp. 483-492, Springer Cham. (acceptance rate  30%)

  5. Grzech, D., Kainz, B., Glocker, B., Le Folgoc, L., “Image Registration via Stochastic Gradient Markov Chain Monte Carlo”. In Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis: Second International Workshop, UNSURE 2020 at MICCAI 2020, and Third International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings (p. 3). Springer Nature. (nurturing talented RA Le Folgoc with last author position)

  6. Vlontzos, A., Budd, S., Hou, B., Rueckert, D., Kainz, B., “3D Probabilistic Segmentation and Volumetry from 2D projection images”. In International Workshop on Thoracic Image Analysis 2020 at MICCAI 2020 Oct 8 (pp. 48-57). Springer, Cham.

  7. Meng, Q., Rueckert, D., Kainz, B., “Unsupervised Cross-domain Image Classification by Distance Metric Guided Feature Alignment.” In Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis 2020 at MICCAI 2020 Oct 4 (pp. 146-157). Springer, Cham.

  8. Tan, J., Au, A., Meng, Q., Finesilver-Smith, S., Simpson, J., Rueckert, D., Razavi, R., Day, T., Lloyd, D., Kainz, B., “Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening”. In Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis 2020 at MICCAI 2020 Oct 4 (pp. 243-252). Springer, Cham.

  9. Tan, J., Kainz, B., “Divergent search for image classification behaviors.” In Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion 2020 Jul 8 (pp. 91-92).

  10. Miolane, N., Guigui, N., Zaatiti, H., Shewmake, C., Hajri, H., Brooks, D., Le Brigant, A., Mathe, J., Hou, B., Thanwerdas, Y., Heyder, S., Peltre, O., Koep, N., Cabanes, Y., Gerald, Th., Chauchat, P., Kainz, B., Donnat, C., Holmes, S., Pennec, X., “Introduction to Geometric Learning in Python with Geomstats.” In Proceedings of the 19th Python in Science Conference 2020 Jul 6 (Vol. 2020).

  11. Hou, B., Vlontzos, A., Alansary, A., Rueckert, D., Kainz, B., “Flexible Conditional Image Generation of Missing Data with Learned Mental Maps.” In International Workshop on Machine Learning for Medical Image Reconstruction 2019 at MICCAI 2019 Oct 17 (pp. 139-150). Springer, Cham.

  12. Meng, Q., Pawlowski, N., Rueckert, D., Kainz, B., “Representation disentanglement for multi-task learning with application to fetal ultrasound.” In Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis 2019 at MICCAI 2019 Oct 17 (pp. 47-55). Springer, Cham.

  13. Chotzoglou, E., Kainz, B., “Exploring the Relationship Between Segmentation Uncertainty, Segmentation Performance and Inter-observer Variability with Probabilistic Networks.” In Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 2019 at MICCAI 2019 Oct 13 (pp. 51-60). Springer, Cham.

  14. Wright, R., Toussaint, N., Gomez, A., Zimmer, V., Khanal, B., Matthew, J., Skelton, E., Kainz, B., Rueckert, D., Hajnal, J.V., Schnabel, J.A., “Complete Fetal Head Compounding from Multi-view 3D Ultrasound.” In International Conference on Medical Image Computing and Computer-Assisted Intervention 2019 at MICCAI 2019 Oct 13 (pp. 384-392). Springer, Cham. (acceptance rate  30%)

  15. Tan, J., Au, A., Meng, Q., Kainz, B., “Semi-supervised Learning of Fetal Anatomy from Ultrasound.” In Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data 2019 at MICCAI 2019 Oct 13 (pp. 157-164). Springer, Cham.

  16. Holland, R., Patel, U., Lung, P., Chotzoglou, E., Kainz, B., “Automatic detection of bowel disease with residual networks.” In International Workshop on PRedictive Intelligence In MEdicine 2019 at MICCAI 2019 Oct 13 (pp. 151-159). Springer, Cham.

  17. Budd, S., Sinclair, M., Khanal, B., Matthew, J., Lloyd, D., Gomez, A., Toussaint, N., Robinson, E.C., Kainz, B., Confident head circumference measurement from ultrasound with real-time feedback for sonographers. In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2019 Oct 13 (pp. 683-691). Springer, Cham. (acceptance rate  30%)

  18. Vlontzos, A., Alansary, A., Kamnitsas, K., Rueckert, D., Kainz, B., “Multiple landmark detection using multi-agent reinforcement learning.” In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2019 Oct 13 (pp. 262-270). Springer, Cham. (acceptance rate  30%)

Peer reviewed conference abstracts:

  1. Ellershaw, S., Schmidtke, L., Khatib, N., Eden, J., Jones, A., Dall’Orso, S., Muceli, S., Burdet, E., Nowlan, N., Arichi, T., Kainz, B., "3D Infant Pose Estimation Using Transfer Learning", Medical Imaging meets NeurIPS 2020 (oral, acceptance rat 14%)

  2. Hou, B., Kaissis, G., Summers, R., Kainz, B., "RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting", Medical Imaging meets NeurIPS 2020

  3. Chotzoglou, E., Budd, S., Day, Th., Simpson, J., Kainz, B., "Unsupervised detection of Hypoplastic Left Heart Syndrome in fetal screening", Medical Imaging meets NeurIPS 2020

Public source code and demos:

Preprints:

  1. Vlontzos, A., Rocha, H.B., Rueckert, D., Kainz, B., “Causal Future Prediction in a Minkowski Space-Time.” arXiv preprint arXiv:2008.09154. 2020 Aug 20. (MIT tech review feature story, https://bit.ly/35cN6rq )

  2. Budd, S., Robinson, E.C., Kainz B., “A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis”. arXiv preprint arXiv:1910.02923. 2019 Oct 7.

  3. Jiang, G., Kainz, B., “One Shot Radiance: Global Illumination Using Convolutional Autoencoders.” arXiv preprint arXiv:1910.02480. 2019 Oct 6.

  4. Meng, Q., Matthew, J., Zimmer, V.A., Gomez, A., Lloyd, D.F., Rueckert, D., Kainz, B., “Mutual Information-based Disentangled Neural Networks for Classifying Unseen Categories in Different Domains: Application to Fetal Ultrasound Imaging.” arXiv preprint arXiv:2011.00739. 2020 Oct 30.

  5. Hinterreiter, A., Streit, M., Kainz, B., “Projective Latent Space Decluttering.” arXiv preprint arXiv:2006.12902. 2020 Jun 23.

  6. Liu, T., Meng, Q., Vlontzos, A., Tan, J., Rueckert, D., Kainz, B., “Ultrasound Video Summarization using Deep Reinforcement Learning.” arXiv preprint arXiv:2005.09531. 2020 May 19.

  7. Tan, J., Kainz, B., “Divergent Search for Few-Shot Image Classification.” arXiv preprint arXiv:2004.07903. 2020 Apr 16.

  8. Meng, Q., Rueckert, D., Kainz, B., “Learning Cross-domain Generalizable Features by Representation Disentanglement.” arXiv preprint arXiv:2003.00321. 2020 Feb 29.

  9. Budd, S., Patkee, P., Baburamani, A., Rutherford, M., Robinson, E.C., Kainz, B., “Surface Agnostic Metrics for Cortical Volume Segmentation and Regression.” arXiv preprint arXiv:2010.01669. 2020 Oct 4.

  10. Robinson, R., Valindria, V.V., Bai, W., Oktay, O., Kainz, B., Suzuki, H., Sanghvi, M.M., Aung, N., Paiva, J., Zemrak, F., Fung, K. Paiva, J.M., Carapella, V., Kim, Y.J., Suzuki, H., Kainz, B., Matthews, P.M., Petersen, St. E., Piechnik, St. K., Neubauer, St., Glocker, B., Rueckert, D., “Automated Quality Control in Image Segmentation: Application to the UK Biobank” Cardiac MR Imaging Study. arXiv preprint arXiv:1901.09351. 2019 Jan 27.

  11. Bakas, S., …, Kainz, B., …, et al. (327+ authors) “Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge.” arXiv preprint arXiv:1811.02629. 2018 Nov 5.

Contributions from 2015 to 2018:

peer reviewed Q1 (scimagojr.com) journal papers:

  1. Oktay, O., Ferrante , E., Kamnitsas , K., Heinrich, M., Bai, W., Caballero, J., Cook, S. A., de Marvao, A., Dawes, T., O’Regan, D. P., Kainz, B., Glocker, B., and Rueckert, D., “Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation” IEEE Trans. Med Imag 37, (2018), 384-395.

  2. Lloyd, D. F. A., van Poppel, M., Schultz, A., Pushparajah, K., Simpson, J., van Amerom, J.F.P., Kainz, B., Kuklisova-Murgasova, M., Vigneswaran, T., Charakida, M., Miller, O., Zidere, V., Sharland, G., Rutherford, M., Hajnal, J., and Razavi, R., “Motion corrected fetal cardiac MRI increases diagnostic confidence in clinically” challenging cases, Heart 104, (2018), A11–A11

  3. Verbruggen, St. W., Kainz, B., Shelmerdine, S. C., Hajnal, J. V., Rutherford, M. A, Arthurs, O. J., Phillips, A. T. M., and Nowlan, N. C., “Stresses and strains on the human fetal skeleton during development”, J. Royal Soc. Interface 15(138), (2018), 20170593

  4. Verbruggen, S.W., Kainz, B., Shelmerdine, S.C., Arthurs, O.J., Hajnal, J.V., Rutherford, M.A., Phillips, A.T., and Nowlan, N.C., "Altered biomechanical stimulation of the developing hip joint in presence of hip dysplasia risk factors", J Biomech 78, (2018), 1 - 9

  5. Hou, B., Khanal, B., Alansary, A., McDonagh, St., Davidson, A., Rutherford, M., Hajnal, J. V., Rueckert, D., Glocker, B., and Kainz, B., “3-D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Images”, IEEE Trans Med Imag 37, (2018), 1737-1750

  6. Alansary, A., Rajchl, M., McDonagh, S. G., Murgasova, M., Damodaram, M., Lloyd, D. F. A., Davidson, A., Rutherford, M., Hajnal, J. V., Rueckert, D., and Kainz, B., “PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI”, IEEE Trans Med Imag 36, (2017), 2031-2044

  7. Miao, H., Mistelbauer, G., Karimov, A., Alansary, A., Davidson, A., Lloyd, D. F. A., Damodaram, M., Story, L., Hutter, J., Hajnal, J. V., Rutherford, M., Preim, B., Kainz, B., and Gröller, M. E., “Placenta Maps: In Utero Placental Health Assessment of the Human Fetus”, IEEE Trans Vis Comput Grap 23, (2017),1612-1623

  8. Rajchl, M., Lee, M. C. H., Oktay, O., Kamnitsas, K., Passerat-Palmbach, J., Bai, W., Damodaram, M., Rutherford, M. A., Hajnal, J. V., Kainz, B., and Rueckert, D., “DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks”, IEEE Trans Med Imag 36, (2017), 674-683

  9. Baumgartner, C. F., Kamnitsas, K., Matthew, J., Fletcher, T. P., Smith, S., Koch, L. M., Kainz, B., and Rueckert, D., “SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound”, IEEE Trans Med Imag 36, (2017), 2204-2215

  10. Lloyd, D., Kainz, B., van Amerom, J. F., Lohezic, M., Pushparajah, K., Simpson, J. M., Malamateniou, Ch., Hajnal, J. V., Rutherford, M., and Razavi, R., “Prenatal MRI visualisation of the aortic arch and fetal vasculature using motion-corrected slice-to-volume reconstruction”, J Cardiovasc Magn Reson 18, (2016), 180

  11. Rueckert, D., Glocker, B., and Kainz, B., “Learning clinically useful information from images: Past, present and future “, Med Image Anal 33, (2016), 13 - 18

  12. Egger, J., Busse, H., Brandmaier, P., Seider, D., Gawlitza, M., Strocka, S., Voglreiter, P., Dokter, M., Hofmann, M., Kainz, B., Hann, A., Chen, X., Alhonnoro, T., Pollari, M.; Schmalstieg, D., and Moche, M., “Interactive Volumetry Of Liver Ablation Zones”, Scientific Reports, 5, (2015), 15373

  13. Kainz, B., Steinberger, M., Wein, W., Kuklisova-Murgasova, M., Malamateniou, C., Keraudren, K., Torsney-Weir, T., Rutherford, M., Aljabar, P., Hajnal, J.V., and Rueckert, D., “Fast Volume Reconstruction From Motion Corrupted Stacks of 2D Slices”, IEEE Trans Med Imag 34, (2015),1901–1913 Books edited:

  14. Stoyanov, D.; Taylor, Z.; Kainz, B.; Maicas, G.; Beichel, R.; Martel, A.; Maier-Hein, L.; Bhatia, K; Vercauteren, T., Oktay, O. T., Carneiro O., Carneiro, G.; Bradley, A. P.; Nascimento, J.; Min, H.; Brown, M. S.; Jacobs, C.; Lassen-Schmidt, B.; Mori, K.; Petersen, J.; Estépar ,R. S. J.; Schmidt-Richberg, A.; Veiga, C. (Eds.), “Image Analysis for Moving Organ, Breast, and Thoracic Images: Third International Workshop, RAMBO 2018, Fourth International Workshop, BIA 2018, and First International Workshop, TIA 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16 and 20, 2018, Proceedings”, Lecture Notes in Computer Science (LNCS) 11040, (Springer International Publishing) 2018

  15. Zuluaga, M. A.; Bhatia, K.; Kainz, B.; Moghari, M. H.; Pace, D. F. (Eds.), Reconstruction, Segmentation, and Analysis of Medical Images: First International Workshops, RAMBO 2016 and HVSMR 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected Papers, volume LNCS 10129, (Springer International Publishing) 2017

  16. Cardoso, J., Arbel, T.; Gao, F.; Kainz, B.; van Walsum, T.; Shi, K.; Bhatia, K. K.; Peter, R.; Vercauteren, T.; Reyes, M.; Dalca, A.; Wiest, R.; Niessen, W.; Emmer, B. J. (Eds.), Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment. Fifth International Workshop, CMMI 2017, Second International Workshop, RAMBO 2017, and First International Workshop, SWITCH 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, 2017, Proceedings, volume 10555, (Springer, Cham) 2017

Peer reviewed full papers at scientific conferences:

  1. Alansary, A.; Le Folgoc, L.; Vaillant, G.; Oktay, O.; Li, Y.; Bai, W.; Passerat-Palmbach, J., Guerrero, R., Kamnitsas, K., Hou, B., McDonagh, S., Glocker, B., Kainz, B., and Rueckert, D., “Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 Frangi, A. F., Schnabel, J. A., Davatzikos, C., Alberola-Lopez, C., and Fichtinger, G. ed (Springer International Publishing) 2018, 277-285 (Proc. 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2018) (acceptance rate  30%)

  2. Tanno, R., Makropoulos, A., Arslan, S., Oktay, O., Mischkewitz, S., Al-Noor, F., Oppenheimer, J., Mandegaran, R., Kainz, B., and Heinrich, M., “AutoDVT: Joint Real-time Classification for Vein Compressibility Analysis in Deep Vein Thrombosis Ultrasound Diagnostics” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 Frangi, A. F., Schnabel, J. A., Davatzikos, C., Alberola-Lopez, C., and Fichtinger, G. ed (Springer International Publishing) 2018, 905-912 (Proc. 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2018) (acceptance rate  30%)

  3. Li, Y., Alansary, A., Cerrolaza, J., Khanal, B., Sinclair, M., Matthew, J., Gupta, C., Knight, C., Kainz, B., and Rueckert, D. “Fast Multiple Landmark Localisation Using a Patch-based Iterative Network” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 Frangi, A. F., Schnabel, J. A., Davatzikos, C., Alberola-Lopez, C., and Fichtinger, G. ed (Springer International Publishing) 2018, 563-571 (Proc. 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2018) (acceptance rate  30%)

  4. Hou, B., Miolane, N., Khanal, B., Lee, M., Alansary, A., McDonagh, S., Hajnal, J., Glocker, B., Rueckert, D., and Kainz, B., “Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry”, in Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 Frangi, A. F., Schnabel, J. A., Davatzikos, C., Alberola-Lopez, C., and Fichtinger, G. ed (Springer International Publishing) 2018, 756-764 (Proc. 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2018) (acceptance rate  30%)

  5. Li, Y., Khanal, B., Hou, B., Alansary, A., Cerrolaza, J., Sinclair, M., Matthew, J., Gupta, C., Knight, C., Kainz, B., and Rueckert, D., “Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 Frangi, A. F., Schnabel, J. A., Davatzikos, C., Alberola-Lopez, C., and Fichtinger, G. ed (Springer International Publishing) 2018, 392-400 (Proc. 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2018) (acceptance rate  30%)

  6. Cerrolaza, J., Li, Y., Biffi, C., Gomez, A., Sinclair, M., Matthew, J., Knight, C. Kainz, B., and Rueckert, D., “3D Fetal Skull Reconstruction from 2DUS via Deep Conditional Generative Networks” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 Frangi, A. F., Schnabel, J. A., Davatzikos, C., Alberola-Lopez, C., and Fichtinger, G. ed (Springer International Publishing) 2018, 383-391 (Proc. 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2018) (acceptance rate  30%)

  7. Robinson, R., Oktay, O., Bai, W., Valindria, V., Kainz, B., Piechnik, S., Neubauer, S., Petersen, S., Page, C., Rueckert, D., Glocker, B., “Real-time Prediction of Segmentation Quality”, in Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 Frangi, A. F., Schnabel, J. A., Davatzikos, C., Alberola-Lopez, C., and Fichtinger, G. ed (Springer International Publishing) 2018, 578-585 (Proc. 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2018) (acceptance rate  30%)

  8. Oktay, O., Schlemper, J., Le Folgoc, L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., Kainz, B., Glocker, B., and Rueckert, D., “Attention U-Net: Learning Where to Look for the Pancreas” in Medical Imaging with Deep Learning 2018, https://openreview.net/forum?id=Skft7cijM, conference track, non-archival, 2018, to appear in Medical Image Analysis (acceptance rate  30%)

  9. Alansary, A., Oktay, O., Li, Y., Le Folgoc, L., Hou, B., Vaillant, G., Glocker, B., Kainz, B., and Rueckert, D. “Evaluating Reinforcement Learning Agents for Anatomical Landmark Detection”, in Medical Imaging with Deep Learning 2018, https://openreview.net/forum?id=SyQK4-nsz, conference track, non-archival, 2018, to appear in Medical Image Analysis (acceptance rate  30%)

  10. Schlemper, J., Oktay, O., Chen, L., Matthew, J., Knight, C., Toussaint, N., Kainz, B., Glocker, B., and Rueckert, D. “Attention-Gated Networks for Improving Ultrasound Scan Plane Detection ”, in Medical Imaging with Deep Learning 2018, https://openreview.net/forum?id=BJtn7-3sM, conference track, non-archival, 2018, (acceptance rate  30%)

  11. Sinclair, M. D., Cerrolaza Martinez, J., Skelton, E., Li, Y., Baumgartner, C. F., Bai, W., Matthew, J., Knight, C. L., Smith, S., Hajnal, J., King, A. P., Kainz, B., and Rueckert, D., “Cascaded Transforming Multi-task Networks For Abdominal Biometric Estimation from Ultrasound”, in Medical Imaging with Deep Learning 2018, https://openreview.net/forum?id=r1ZGQW2if, conference track, non-archival, 2018, (acceptance rate  30

  12. Kamnitsas, K., Bai, W., Ferrante, E., McDonagh, S., Sinclair, M., Pawlowski, N., Rajchl, M., Lee, M., Kainz, B., Rueckert, D. and Glocker, B., “Ensembles of multiple models and architectures for robust brain tumour segmentation” In International MICCAI Brainlesion Workshop 2017 Crimi, A. et al. ed (Springer, Cham.), 2017, 450-462. winner of the MICCAI BraTS 2017 challenge

  13. Sinclair, M., Baumgartner, C. F., Matthew, J., Bai, W., Cerrolaza, J. J. , Li, Y., Smith, S., Knight, C., Kainz, B., Hajnal, J. V., King, A. P., and Rueckert, D., “Human-level performance on automatic head biometrics in fetal ultrasound using fully convolutional neural networks”, in “40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), , Honolulu, HI, USA, 2018” (IEEE) 2018, 714-717 (Proc. EMBC’18, ) (acceptance rate  40%)

  14. McDonagh, S., Hou, B., Kamnitsas, K., Oktay, O., Alansary, A., Rutherford, M., Hajnal, J.V., and Kainz, B., “Context-Sensitive Super-Resolution for Fast Fetal Magnetic Resonance Imaging” in “RAMBO 2017, CMMI 2017, SWITCH 2017: Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment” Zuluaga, M. A.; et al. ed (Springer International Publishing) 2017, 116-126 (Proc. Reconstruction and Analysis of Moving Body Organs (RAMBO)’18) (acceptance rate  50%)

  15. Cerrolaza, J. J., Oktay, O., Gomez, A., Matthew, J., Knight, C., Kainz, B., and Rueckert, D., “Fetal Skull Segmentation in 3D Ultrasound via Structured Geodesic Random Forest” in “Fetal, Infant and Ophthalmic Medical Image Analysis: International Workshop, FIFI 2017, and 4th International Workshop, OMIA 2017, Held in Conjunction with MICCAI 2017, Qu’ebec City, QC, Canada, September 14” Cardoso, M. J. et al. ed (Springer International Publishing) 2017, 25–32 (Proc. Fetal, Infant and Ophthalmic Medical Image Analysis 2017) (acceptance rate  50%)

  16. Hou, B., Alansary, A., McDonagh, S., Davidson, A., Rutherford, M., Hajnal, J. V., Rueckert, D., Glocker, B., and Kainz, B., “Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2017, LNCS 10434 Descoteaux, M. et al. ed (Springer International Publishing) 2017, 296–304 (Proc. 20th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2017) (acceptance rate  30

  17. Toisoul, A., Rueckert, D., Kainz, B., “Accessible GLSL Shader Programming” in “EG 2017 - Education Papers” J. J. Bourdin and A. Shesh ed (Eurographics Proceedings The Eurographics Association) 2017, 35-42 (Proc. 38th Annual Conference of the European Association for Computer Graphics (Eurographics) 2017) (acceptance rate  30%)

  18. Alansary, A., Kamnitsas, K., Rajchl, M., Davidson, A., Khlebnikov, R., Malamateniou, C., Rutherford, M., Hajnal, J., Glocker, B., Rueckert, D., and Kainz, B., “Fast Fully Automatic Segmentation of the Human Placenta from Motion Corrupted MRI” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2016, LNCS 9901 Ourselin, S. et al. ed (Springer International Publishing) 2016, 589–597 (Proc. 19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2016) (acceptance rate  30%)

  19. Kainz, B., Lloyd, D. F.A., Alansary, A., Kuklisova Murgasova, M., Khlebnikov, R., Rueckert, D., Rutherford, M., Razavi, R., and Hajnal, J. V., “High-Performance Motion Correction of Fetal MRI” in “EuroRV3: EuroVis Workshop on Reproducibility, Verification, and Validation in Visualization” Lawonned, K. et al. (The Eurographics Association) 2016, 5–7 (Proc. EuroRV3: EuroVis Workshop on Reproducibility, Verification, and Validation in Visualization 2016)

  20. Lloyd, D. F.A., Kainz, B., van Amerom, J. F. P., Pushparajah, K., Simpson, J. M., Zidere, V., Miller, O., Sharland, G., Zhang, T., Lohezic, M., Allsop, J., Fox, M., Malamateniou, C., Rutherford, M., Hajnal, J., and Razavi, R., “Three-Dimensional Modelling of the Fetal Vasculature from Prenatal MRI using Motion Corrected Slice-to-Volume Registration“ in Int. Soc. Magn. Reson. Med.. Vol. 24 (International Society of Magnetic Resonance in Medicine) 2016, O413 (Proceedings of the International Society of Magnetic Resonance in Medicine (ISMRM 2016)

  21. Baumgartner, C. F., Kamnitsas, K. , Matthew, J., Smith, S., Kainz, B., and Rueckert, D., “Real-Time Standard Scan Plane Detection and Localisation in Fetal Ultrasound Using Fully Convolutional Neural Networks” ” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2016, LNCS 9901 Ourselin, S. et al. ed (Springer International Publishing) 2016, 203–211(Proc. 19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2016) (acceptance rate  30%)

  22. Kainz, B., Alansary, A., Malamateniou, C., Keraudren, K., Rutherford, M., Hajnal, J. V., and Rueckert, D., “Flexible Reconstruction and Correction of Unpredictable Motion from Stacks of 2D Images” in Medical Image Computing and Computer-Assisted Intervention– MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part II, volume 9350 Navab, N. et al. ed (2015), 555–562 (Proc. 18th International Conference on Medical Image Computing and Computer-Assisted Intervention 2015) (acceptance rate  30%)

Peer reviewed conference abstracts:

  1. Li, Y., Cerrolaza, J. J., Sinclair, M., Hou, B., Alansary, A., Khanal, B., Matthew, J., Kainz, B., and Rueckert, D., Standard Plane Localisation in 3D Fetal Ultrasound Using Network with Geometric and Image Loss, Medical Imaging with Deep Learning Conference (MIDL) 2018, https://openreview.net/pdf?id=BykcN8siz , abstract track, non-archival, 2018

  2. Meng, Q., Baumgartner, C., Sinclair, M., Housden, J., Rajchl, M., Gomez, A., Hou, B., Toussaint, N., Tan, J., Matthew, J., Rueckert, D., Schnabel, J., and Kainz, B., “Automatic Shadow Detection in 2D Ultrasound” , Medical Imaging with Deep Learning Conference (MIDL) 2018.

  3. Robinson, R., Oktay, O., Bai, W., Valindria, V. V., Sanghvi, M. M., Aung, N., Paiva, J. M., Zemrak, F., Fung, K., Lukaschuk, E., Lee, A. M., Carapella, V., Kimm, Y. J., Kainz, B., Piechnik, S. K., Neubauer, S., Petersen, S. E., Page, C., Rueckert, D., and Glocker, B. “Subject-level Prediction of Segmentation Failure using Real-Time Convolutional Neural Nets” , Medical Imaging with Deep Learning Conference (MIDL) 2018, https://openreview.net/forum?id=SJJfBr9oM, abstract track, non-archival, 2018

  4. Hou, B., Miolane, N., Khanal, B., Lee, M., Alansary, A., McDonagh, S., Hajnal, J., Rueckert, D., Glocker, B., and Kainz, B., Deep Pose Estimation for Image-Based Registration, Medical Imaging with Deep Learning Conference (MIDL) 2018, https://openreview.net/forum?id=SyweajisG , abstract track, non-archival, 2018

  5. Hou, B., Khanal, B., Alansary, A., McDonagh, S., Davidson, A., Rutherford, M., Hajnal, J. V., Rueckert, D., Glocker, B., and Kainz, B., “Image-Based Registration in Canonical Atlas Space”, Medical Imaging with Deep Learning Conference (MIDL) 2018, https://openreview.net/forum?id=Syxv3ijjf , abstract track, non-archival, 2018

  6. Verbruggen, S. W., Kainz, B., Shelmerdine, S. C., Hajnal, J. V., Rutherford, M. A., Arthurs, O. J., Phillips, A.T.M., and Nowlan, N. C., “Altered Biomechanical Stimulation of the Fetal Skeleton Correlates with Risk Factors for Developmental Dysplasia of the Hip”, peer reviewed poster abstract, ORS Annual meeting of the Orthopaedic Research Society Volume 43 March 10-13 (Orthopaedic Research Society) 0696

  7. Budd, S., Robinson, E. C., Kainz, B., “The Cortical Explorer: A Web-based User-interface for the Exploration of the Human Cerebral Cortex”, in proceedings of the 7th Eurographics Workshop on Visual Computing for Biology and Medicine (Posters), 2017

  8. Verbruggen, S. W., Kainz, B., Arthurs, O. J., Hajnal, J. V., Rutherford, M. A., Phillips, A. T.M., Nowlan, N. C., “Modelling the biomechanics of fetal movements”, XXVI Congress of the International Society of Biomechanics 23 -27 July 2017, Brisbane Convention & Exhibition Centre Brisbane, Australia – International Society of Biomechanics Meeting (International Society of Biomechanics ) 2017, 612

  9. Kim, H., Lau, J., Kainz, B., McGuillen, P., Xu, D., and Peyvandi, S. Fetal brain volumetry to predict neonatal brain injury in patients with congenital heart disease, Organization for Human Brain Mapping (OHBM) 2017 - Organization for Human Brain Mapping, 2017 (Proc 23rd Annual Meeting of the Organization for Human Brain Mapping June 25–29, 2017, Vancouver Convention Centre | Vancouver, British Columbia, Canada)

  10. Verbruggen, S. W., Kainz, B., Hajnal, J. V., Rutherford, M. A., Phillips, A.T.M., Nowlan, N. C., Exploring the Link between Fetal Movements and Hip Dysplasia in Singleton and Twin Pregnancies, the 2017 meeting of the Orthopaedic Research Society, 2016

Preprints:

  1. Meng, Q., Sinclair, M., Zimmer, V., Hou, B., Rajchl, M., Toussaint, N., Gomez, A., Housden, J., Matthew, J., Rueckert, D., Schnabel, J., Kainz, B., "Weakly Supervised Estimation of Shadow Confidence Maps in Ultrasound Imaging", Computer Vision and Pattern Recognition (cs.CV), preprint available on arXiv: 1811.08164 e-prints, 2018

  2. Hou, B., Miolane, N., Khanal, B., Lee, M.C., Alansary, A., McDonagh, S., Hajnal, J.V., Rueckert, D., Glocker, B. and Kainz, B., “Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry”, Computer Vision and Pattern Recognition (cs.CV), preprint available on arXiv: 1805.01026 e-prints, 2018

  3. Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B. and Rueckert, D., “Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images“ Computer Vision and Pattern Recognition (cs.CV), preprint available on arXiv: 1808.08114 e-prints, 2018

  4. Schlemper, J., Oktay, O., Chen, L., Matthew, J., Knight, C., Kainz, B., Glocker, B. and Rueckert, D., “Attention-Gated Networks for Improving Ultrasound Scan Plane Detection”, Computer Vision and Pattern Recognition (cs.CV), preprint available on arXiv1804.05338 e-prints, 2018

  5. Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., and Glocker, B., “Attention U-Net: Learning Where to Look for the Pancreas”, ArXiv e-prints, 2018

  6. Zhou, K., and Kainz, B., “Efficient Image Evidence Analysis of CNN Classification Results”, Computer Vision and Pattern Recognition (cs.CV), preprint available on arXiv: 1801.01693 e-prints, 2018

  7. Pawlowski, N., Ktena, S.I., Lee, M.C., Kainz, B., Rueckert, D., Glocker, B. and Rajchl, M., DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images, Computer Vision and Pattern Recognition (cs.CV), preprint available on arXiv: 1711.06853 e-prints, 2017

  8. Kamnitsas, K., Bai, W., Ferrante, E., McDonagh, S., Sinclair, M., Pawlowski, N., Rajchl, M., Lee, M., Kainz, B., Rueckert, D. and Glocker, B., “Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation”, Computer Vision and Pattern Recognition (cs.CV), preprint available on arXiv: 1711.01468 e-prints, winner of the MICCAI BraTS 2017 challenge, 2017

  9. Bai, W., Sinclair, M., Tarroni, G., Oktay, O., Rajchl, M., Vaillant, G., Lee, A.M., Aung, N., Lukaschuk, E., Sanghvi, M.M. Zemrak, F., Fung, K., Paiva, J.M., Carapella, V., Kim, Y.J., Suzuki, H., Kainz, B., Matthews, P.M., Petersen, S.E., Piechnik, S.K., Neubauer, S., Glocker, B., and Rueckert, D., “Automated cardiovascular magnetic resonance image analysis with fully convolutional networks”, Computer Vision and Pattern Recognition (cs.CV), preprint available on arXiv: 1710.09289 e-prints, 2017

  10. Hou, B., Khanal, B., Alansary, A., McDonagh, S., Davidson, A., Rutherford, M., Hajnal, J.V., Rueckert, D., Glocker, B. and Kainz, B., “3D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Images”, Computer Vision and Pattern Recognition (cs.CV), preprint available on arXiv: 1709.06341, 2017

  11. Baumgartner, C.F., Kamnitsas, K., Matthew, J., Fletcher, T.P., Smith, S., Koch, L.M., Kainz, B., and Rueckert, D., “Real-Time Detection and Localisation of Fetal Standard Scan Planes in 2D Freehand Ultrasound”, Computer Vision and Pattern Recognition (cs.CV), preprint available on arXiv: 1612.05601 e-prints, 2016

  12. Rajchl, M., Lee, M.C., Schrans, F., Davidson, A., Passerat-Palmbach, J., Tarroni, G., Alansary, A., Oktay, O., Kainz, B. and Rueckert, “Learning under Distributed Weak Supervision”, Computer Vision and Pattern Recognition (cs.CV), preprint available on arXiv :1606.01100, 2016

  13. Rajchl, M., Lee, M.C., Oktay, O., Kamnitsas, K., Passerat-Palmbach, J., Bai, W., Damodaram, M., Rutherford, M.A., Hajnal, J.V., Kainz, B., and Rueckert, D., “DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks”, Computer Vision and Pattern Recognition (cs.CV), preprint available on arXiv:1605.07866, 2016

  14. Oktay, O., Ferrante, E., Kamnitsas, K., Heinrich, M., Bai, W., Caballero, J., Cook, S.A., de Marvao, A., Dawes, T., O‘Regan, D.P., Kainz, B., Glocker, B., Rueckert, D., “Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation”, CoRR, volume abs/1705.08302, 2017

  15. Alansary, A., Rajchl, M., McDonagh, S.G., Murgasova, M., Damodaram, M., Lloyd, D.F., Davidson, A., Rutherford, M., Hajnal, J.V., Rueckert, D. and Kainz, B., “PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI”, Computer Vision and Pattern Recognition (cs.CV), preprint available on arXiv:1611.07289, 2016, Computer Vision and Pattern Recognition (cs.CV)

Public source code:

  1. https://github.com/bkainz/ShaderLabFramework Desktop Teaching framework for Computer Graphics (updated in 2018)

  2. https://github.com/bkainz/DeepPose pose estimation with CNNs and Riemannian metrics in SE(3)

  3. https://github.com/bkainz/Attention-Gated-Networks attention gated networks for segmentation and classification

  4. http://www.corticalexplorer.com/ outreach framework to visualise cortical brain function

  5. SonoNet https://github.com/baumgach/SonoNet-weights framework for real-time fetal standard scan plane detection during ultrasound examinations

  6. https://github.com/bkainz/fetalReconstruction PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI

  7. https://github.com/bkainz/fetalReconstruction GPU accelerated Motion compensation for medical images

Contributions before 2015:

  1. Kainz, B., Malamateniou, C., Ferrazzi, G., Murgasova, M., Egger, J., Keraudren, K., Rutherford, M., Hajnal, J.V. and Rueckert, D., 2015, April. “Adaptive scan strategies for fetal MRI imaging using slice to volume techniques”. In Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on(pp. 849-852). IEEE.

  2. Egger, J., Voglreiter, P., Dokter, M., Hofmann, M., Busse, H., Seider, D., Brandmaier, P., Rautio, R., Zettel, G., Schmerböck, B. and van Amerongen, M., Jenniskens, S., Kolesnik, M., Kainz, B., Sequeiros R.B., Portugaller, H., Stiegler, P., Futterer, J., Schmalstieg, D., Moche, M., “In-depth Multicenter Workflow Analysis of Liver Tumor Ablations for the Development of a Novel Computer-aided Software Tool”, RSNA Annual Meeting - Radiological Society of North America, 101th Annual Meeting of The Radiological Society of North America (RSNA), 2015

  3. Alansary, A., Lee, M., Keraudren, K., Kainz, B., Malamateniou, C., Rutherford, M., Hajnal, J.V., Glocker, B. and Rueckert, D., 2015, July. “Automatic brain localization in fetal MRI using superpixel graphs”. In Medical Learning Meets Medical Imaging (pp. 13-22). Springer, Cham.

  4. Egger, J., Busse, H., Brandmaier, P., Seider, D., Gawlitza, M., Strocka, S., Voglreiter, P., Dokter, M., Hofmann, M., Kainz, B. and Chen, X., 2015, August. RFA-cut: semi-automatic segmentation of radiofrequency ablation zones with and without needles via optimal st-cuts. In Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE (pp. 2423-2429). IEEE.

  5. Keraudren, K., Kainz, B., Oktay, O., Kyriakopoulou, V., Rutherford, M., Hajnal, J.V. and Rueckert, D., 2015, October. “Automated localization of fetal organs in MRI using random forests with steerable features”. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 620-627). Springer, Cham.

  6. Bowles, C., Nowlan, N.C., Hayat, T.T., Malamateniou, C., Rutherford, M., Hajnal, J.V., Rueckert, D. and Kainz, B., 2015, March. “Machine learning for the automatic localisation of foetal body parts in cine-MRI scans”. In Medical Imaging 2015: Image Processing (Vol. 9413, p. 94130N). International Society for Optics and Photonics.

  7. Keraudren, K., Kuklisova-Murgasova, M., Kyriakopoulou, V., Malamateniou, C., Rutherford, M.A., Kainz, B., Hajnal, J.V. and Rueckert, D., 2014. Automated fetal brain segmentation from 2D MRI slices for motion correction. NeuroImage, 101, pp.633-643.

  8. Kainz, B., Keraudren, K., Kyriakopoulou, V., Rutherford, M., Hajnal, J.V. and Rueckert, D., 2014, April. “Fast fully automatic brain detection in fetal MRI using dense rotation invariant image descriptors”. In Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on (pp. 1230-1233). IEEE.

  9. Kainz, B., Voglreiter, P., Sereinigg, M., Wiederstein-Grasser, I., Mayrhauser, U., Köstenbauer, S., Pollari, M., Khlebnikov, R., Seise, M., Alhonnoro, T. and Häme, Y., 2014, April. High-resolution contrast enhanced multi-phase hepatic Computed Tomography data fromaporcine Radio-Frequency Ablation study. In Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on (pp. 81-84). IEEE.

  10. Kainz, B., Malamateniou, C., Murgasova, M., Keraudren, K., Rutherford, M., Hajnal, J.V. and Rueckert, D., 2014, September. “Motion corrected 3D reconstruction of the fetal thorax from prenatal MRI”. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 284-291). Springer, Cham.

  11. Steinberger, M., Kenzel, M., Kainz, B., Wonka, P. and Schmalstieg, D., 2014, May. „On‐the‐fly generation and rendering of infinite cities on the GPU”. In Computer graphics forum (Vol. 33, No. 2, pp. 105-114) 146. Steinberger, M., Kenzel, M., Kainz, B., Müller, J., Peter, W. and Schmalstieg, D., 2014, May. „Parallel generation of architecture on the GPU”. In Computer graphics forum (Vol. 33, No. 2, pp. 73-82)

  12. Khlebnikov, R., Voglreiter, P., Steinberger, M., Kainz, B., and Schmalstieg, D., 2014, June. Parallel Irradiance Caching for Interactive Monte‐Carlo Direct Volume Rendering. In Computer graphics forum (Vol. 33, No. 3, pp. 61-70)

  13. Khlebnikov, R., Kainz, B., Steinberger, M. and Schmalstieg, D., 2013. Noise-based volume rendering for the visualization of multivariate volumetric data. IEEE transactions on visualization and computer graphics, 19(12), pp.2926-2935.

  14. Voglreiter, P., Steinberger, M., Kainz, B ., Khlebnikov, R., Schmalstieg, D., “Dynamic GPU Scheduling for Volume Rendering”, IEEE Scientific Visualization, IEEE Scientific Visualization 2013, 2013, Best poster honourable mention for IEEE SciVis 2013

  15. #Kerbl, B., Voglreiter, P., Khlebnikov, R., Schmalstieg, D., Seider, D., Moche, M., Stiegler, P., Portugaller, R.H. and Kainz, B., 2012, October. „Intervention planning of hepatocellular carcinoma radio-frequency ablations”. In Workshop on Clinical Image-Based Procedures (pp. 9-16). Springer, Berlin, Heidelberg.

  16. Seider, D., Kolesnik, M., Kainz, B., Payne, S., Flanagan, R., Pollari, M., Stiegler, P. and Moche, M., 2013, April. “Entwicklung einer komplexen Softwareumgebung für die patientenspezifische Planung und Simulation der Radiofrequenzablation (RFA) von Lebertumoren“. In RöFo-Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren (Vol. 185, No. S 01, p. VO301_4).

  17. Kainz, B., Hauswiesner, S., Reitmayr, G., Steinberger, M., Grasset, R., Gruber, L., Veas, E., Kalkofen, D., Seichter, H. and Schmalstieg, D., 2012, December. “OmniKinect: real-time dense volumetric data acquisition and applications.” In Proceedings of the 18th ACM symposium on Virtual reality software and technology (pp. 25-32). ACM.

  18. Khlebnikov, R., Kainz, B., Steinberger, M., Streit, M. and Schmalstieg, D., 2012, June. „Procedural Texture Synthesis for Zoom‐Independent Visualization of Multivariate Data”. In Computer graphics forum (Vol. 31, No. 3pt4, pp. 1355-1364). Oxford, UK: Blackwell Publishing Ltd.

  19. Steinberger, M., Kenzel, M., Kainz, B., and Schmalstieg, D., 2012, May. „Scatteralloc: Massively parallel dynamic memory allocation for the GPU”. In Innovative Parallel Computing (InPar), 2012 (pp. 1-10). IEEE.

  20. Steinberger, M., Kainz, B., Kerbl, B., Hauswiesner, S., Kenzel, M. and Schmalstieg, D., 2012. „Softshell: dynamic scheduling on GPUs”. ACM Transactions on Graphics (TOG), 31(6), p.161.

  21. #Voglreiter, P., Steinberger, M., Schmalstieg, D. and Kainz, B., 2012, October. „Volumetric real-time particle-based representation of large unstructured tetrahedral polygon meshes”. In Workshop on Mesh Processing in Medical Image Analysis (pp. 159-168). Springer, Berlin, Heidelberg.

  22. Steinberger, M., Kainz, B., Hauswiesner, S., Khlebnikov, R., Kalkofen, D. and Schmalstieg, D., „Ray prioritization using stylization and visual saliency”. Computers & Graphics, 36(6), 2012, 673-684.

  23. Khlebnikov, R., Kainz, B., Muehl, J. and Schmalstieg, D., 2011. „Crepuscular rays for tumor accessibility planning”. IEEE Transactions on Visualization & Computer Graphics, (12), pp.2163-2172.

  24. Mühl, J., Köstenbauer, S., Seise, M., Kainz, B., Stiegler, P., Mayrhauser, U., Portugaller, R.H., Fusion von CT Volumen und histologischen Schnitten orientiert an natürlichen Merkmalspunkten, 2011, DE Patent 102,010,042,073

  25. Koestenbauer, S., Stiegler, P., Stadlbauer, V., Mayrhauser, U., Leber, B., Blattl, D., Kainz, B., Reich, O., Portugaller, R.H., Wiederstein-Grasser, I. and Tschliessnigg, K.H., 2011. Visualization of large-scale sections. J Surg Radiol, 2, pp.170-3.

  26. Khlebnikov, R., Kainz, B., Roth, B., Muehl, J. and Schmalstieg, D.,. „GPU Based On-the-fly Light Emission-absorption Approximation for Direct Multi-volume Rendering”. In Eurographics (Posters) 2011, 11-12.

  27. Kainz, B., 2011. Ray-based image generation for advanced medical applications. PhD Thesis, Graz University of Technology

  28. Kainz, B., Steinberger, M., Hauswiesner, S., Khlebnikov, R. and Schmalstieg, D.,. Stylization-based ray prioritization for guaranteed frame rates. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Non-Photorealistic Animation and Rendering (43-54). 2011, August ACM.

  29. Kainz, B., Steinberger, M., Hauswiesner, S., Khlebnikov, R., Kalkofen, D. and Schmalstieg, D., 2011, February. Using perceptual features to prioritize ray-based image generation. In Symposium on Interactive 3D Graphics and Games (pp. 215-215). ACM.

  30. Kainz, B., Portugaller, R.H., Seider, D., Moche, M., Stiegler, P. and Schmalstieg, D., 2011, September. “Volume visualization in the clinical practice”. In Workshop on Augmented Environments for Computer-Assisted Interventions (pp. 74-84). Springer, Berlin, Heidelberg.

  31. Kainz, B., Steinberger, M., Hauswiesner, S., Khlebnikov, R. and Schmalstieg, D., 2011, August. ”Stylization-based ray prioritization for guaranteed frame rates”. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Non-Photorealistic Animation and Rendering (pp. 43-54). ACM.

  32. Muehl, J., Kainz, B., Bornik, A., Grabner, M., Hauswiesner, S. and Schmalstieg, D., 2009. The Future of Volume Graphics in Medical Virtual Reality. In World Congress on Medical Physics and Biomedical Engineering, September 7-12, 2009, Munich, Germany (pp. 1349-1352). Springer, Berlin, Heidelberg.

  33. Alhonnoro, T., Pollari, M., Lilja, M., Flanagan, R., Kainz, B., Muehl, J., Mayrhauser, U., Portugaller, H., Stiegler, P. and Tscheliessnigg, K., , September. “Vessel segmentation for ablation treatment planning and simulation. In International Conference on Medical Image Computing and Computer-Assisted Intervention”. Springer, Berlin, Heidelberg, 2010, 45-52.

  34. Kainz, B., Grabner, M., Bornik, A., Hauswiesner, S., Muehl, J. and Schmalstieg, D., , December. „Ray casting of multiple volumetric datasets with polyhedral boundaries on manycore GPUs”. In ACM Transactions on Graphics (TOG) 28, No. 5, (ACM) 2009, 152.

  35. Muehl, J.K., Kainz, B.K., Portugaller, H.R., Stiegler, P.B. and Bauer, C.H., , September. „Computer oriented image acquisition of the liver: Toward a better numerical model for radiofrequency ablation”. In Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE (IEEE) 2009, 3755-3758..

  36. Kainz, B., Reiter, U., Reiter, G. and Schmalstieg, D.,. „In vivo interactive visualization of four-dimensional blood flow patterns”. The visual computer, 25(9), 2009, 853-862.

  37. Reiter, U., Reiter, G., Kovacs, G., Schmidt, K., Maier, R., Kainz, B., Olschewski, H. and Rienmueller, R., 2009, April. „Magnetresonanz-basierte Messung des erhöhten, mittleren pulmonalarteriellen Drucks“. In RöFo-Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren(Vol. 181, No. S 01, p. VO329_6).

  38. Reiter, G., Reiter, U., Kovacs, G., Kainz, B., Schmidt, K., Maier, R., Olschewski, H. and Rienmueller, R., “Magnetic Resonance–Derived 3-Dimensional Blood Flow Patterns in the Main Pulmonary Artery as a Marker of Pulmonary Hypertension and a Measure of Elevated Mean Pulmonary Arterial Pressure” CLINICAL PERSPECTIVE. Circulation: Cardiovascular Imaging, 1(1), 2008, 23-30

  39. Grabner, M., Pock, T., Gross, T. and Kainz, B.,.” Automatic differentiation for GPU-accelerated 2D/3D registration”. In Advances in Automatic Differentiation 2008, 259-269). Springer, Berlin, Heidelberg.

  40. Wagner, D., Schmalstieg, D. and Kainz, B., “Realtime 3D graphics programming using the Quake3 engine”. CGEMS: Computer graphics educational materials source, pp.8–8, 2008

  41. Kainz, B., Grabner, M. and Ruether, M., “Fast marker based C-arm pose estimation” in 11th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) Metaxas, D. et al. ed (Springer, Berlin, Heidelberg), 2008, 652-659