A Probabilistic U-Net For Segmentation Of Ambiguous Images

Authors:
Simon Kohl German Cancer Research Center (DKFZ)
Bernardino Romera-Paredes DeepMind
Clemens Meyer DeepMind
Jeffrey De Fauw DeepMind
Joseph R. Ledsam DeepMind
Klaus Maier-Hein German Cancer Research Center
Ali Eslami DeepMind
Danilo Jimenez Rezende Google DeepMind
Olaf Ronneberger DeepMind

Introduction:

Many real-world vision problems suffer from inherent ambiguities.To this end the authors propose a generative segmentation model based on a combination of a U-Net with a conditional variational autoencoder that is capable of efficiently producing an unlimited number of plausible hypotheses.

Abstract:

Many real-world vision problems suffer from inherent ambiguities. In clinical applications for example, it might not be clear from a CT scan alone which particular region is cancer tissue. Therefore a group of graders typically produces a set of diverse but plausible segmentations. We consider the task of learning a distribution over segmentations given an input. To this end we propose a generative segmentation model based on a combination of a U-Net with a conditional variational autoencoder that is capable of efficiently producing an unlimited number of plausible hypotheses. We show on a lung abnormalities segmentation task and on a Cityscapes segmentation task that our model reproduces the possible segmentation variants as well as the frequencies with which they occur, doing so significantly better than published approaches. These models could have a high impact in real-world applications, such as being used as clinical decision-making algorithms accounting for multiple plausible semantic segmentation hypotheses to provide possible diagnoses and recommend further actions to resolve the present ambiguities.

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