Research Staff

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University College London,
818 Malet Place Engineering Building
Malet Place
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Miscellaneous Information

My research interests include mathematical models and machine learning techniques applied to computer vision and medical imaging applications. Before joining the group as a research associate in February 2017, I received a French engineering degree from the Ecole Centrale Paris in 2011 and a Master of Science from the Ecole Normale Superieure de Cachan in applied mathematics. I was a PhD candidate from 2012 to 2016 at the Technical University of Munich under the supervision of Prof. Nassir Navab. During my doctoral studies, I focused on the design of interactive and adaptive approaches facilitating the integration of learned models in the clinical workflow. The contributions of the thesis included an interactive tool based on decision forests to assist the screening of large digital slides in histopathology, as well as hands-free interactive techniques for image segmentation.

 

Current research within the GIFT-Surg project:

Twin-to-twin transfusion syndrome is a complication which can affect true twins sharing a same placenta. In the presence of blood vessels connecting the fetuses, a blood imbalance may occur between the twins resulting in an abnormal volume of blood in both twins. A curative procedure consists in the coagulation of the interconnecting blood vessels which are located on the placenta. However, the fetoscopic video guidance used during surgery only offers a very limited field of view to the surgeon, which can result in missed vascular connections. I have joined the GIFT-Surg project to develop mosaicing techniques enabling the creation of a global map of the placenta from the video sequence acquired during surgery. I focus more specifically on the development of algorithms offering an increased robustness to the visual challenges encountered in surgical conditions such as visual occlusions and deformations due to the non planarity of the placenta.

 

List of publications:

Bieth, M., Peter, L., Nekolla, S. G., Eiber, M., Langs, G., Schwaiger, M., & Menze, B. Segmentation of Skeleton and Organs in Whole-Body CT Images via Iterative Trilateration, IEEE Transactions on Medical Imaging, 2017

Gutiérrez-Becker, B., Peter, L., Klein, T., & Wachinger, C. A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2017

Gutiérrez-Becker, B., Mateus, D., Peter, L., & Navab, N. Guiding Multimodal Registration with Learned Optimization Updates, Medical Image Analysis, 2017

Peter, L., Mateus, D., Chatelain, P., Declara, D., Schworm, N., Stangl, S., Multhoff, G., & Navab, N. Assisting the Examination of Large Histopathological Slides with Adaptive Forests, Medical image analysis, 2017

Dubost, F., Peter, L., Rupprecht, C., Gutiérrez-Becker, B., & Navab, N. Hands-Free Segmentation of Medical Volumes via Binary Inputs, International MICCAI Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, 2016

Gutiérrez-Becker, B., Mateus, D., Peter, L., & Navab, N. Learning Optimization Updates for Multimodal Registration, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2016

Conjeti, S., Katouzian, A., Roy, A. G., Peter, L., Sheet, D., Carlier, S., Laine, A., Navab, N. Supervised Domain Adaptation of Decision Forests: Transfer of Models Trained In Vitro for In Vivo Intravascular Ultrasound Tissue Characterization, Medical image analysis, 2016

Rupprecht, C., Peter, L., & Navab, N. Image Segmentation in Twenty Questions, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015

Peter, L., Pauly, O., Chatelain, P., Mateus, D., & Navab, N. Scale-Adaptive Forest Training via an Efficient Feature Sampling Scheme, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2015

Stauder, R., Okur, A. I., Peter, L., Schneider, A., Kranzfelder, M., Feussner, H., & Navab, N. Random Forests for Phase Detection in Surgical Workflow Analysis, International Conference on Information Processing in Computer-Assisted Interventions (IPCAI), 2014

Peter, L., Mateus, D., Chatelain, P., Schworm, N., Stangl, S., Multhoff, G., & Navab, N. Leveraging Random Forests for Interactive Exploration of Large Histological Images, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2014

Kuipers, D., Mehonic, A., Kajita, M., Peter, L., Fujita, Y., Duke, T., Charras, G., & Gale, J. E. Epithelial Repair Is a Two-Stage Process Driven First by Dying Cells and then by their Neighbours, Journal of Cell Science, 2014

Chatelain, P., Pauly, O., Peter, L., Ahmadi, S. -. A., Plate, A., Bötzel, K., & Navab, N. Learning from Multiple Experts with Random Forests: Application to the Segmentation of the Midbrain in 3D Ultrasound, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2013

Peter, L., Pauly, O., Jansen, S. B. G., Smethurst, P. A., Ouwehand, W. H., & Navab, N. Automatic Event Detection within Thrombus Formation Based on Integer Programming, International MICCAI Workshop on Medical Computer Vision, 2012

Peter, L., Brieu, N., Jansen, S., Smethurst, P. A., Ouwehand, W. H., & Navab, N. Automatic Segmentation and Tracking of Thrombus Formation within In Vitro Microscopic Video Sequences, IEEE International Symposium on Biomedical Imaging (ISBI), 2012

Harris, A. R., Peter, L., Bellis, J., Baum, B., Kabla, A. J., & Charras, G. T. Characterizing the Mechanics of Cultured Cell Monolayers, Proceedings of the National Academy of Sciences, 2012