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14 March 2019 The U-net and its impact to medical imaging (Conference Presentation)
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The U-net has become the predominant choice when facing any medical image segmentation task. This is due to its high performance in many different medical domains. In this talk, I will introduce the U-net, and I will present three projects from DeepMind Health Research that use the U-net to address different challenges. The first project, a collaboration with University College London Hospital, deals with the challenging task of the precise segmentation of radiosensitive head and neck anatomy in CT scans, an essential input for radiotherapy planning. The second project, together with Moorfields Eye Hospital, developed a system that analyses 3D OCT (optical coherence tomography) eye scans to provide referral decisions for patients. The performance was on par with world experts with over 20 years experience. Finally, I will focus on the third project, which deals with the segmentation of ambiguous images. This is of particular relevance in medical imaging where ambiguities can often not be resolved from the image context alone. We propose a combination of a U-net with a conditional variational autoencoder that is capable of efficiently producing an unlimited number of plausible segmentation map hypotheses for a given ambiguous image. We show that each hypothesis provides a globally consistent segmentation, and that the probabilities of these hypotheses are well calibrated.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bernardino Romera-Paredes "The U-net and its impact to medical imaging (Conference Presentation)", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109501G (14 March 2019);

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