%V 12261
%S Lecture Notes in Computer Science
%D 2020
%C Lima, Peru
%K Domain adaptation, Image synthesis, GANs, Segmentation,
MRI
%B Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
%T Harnessing uncertainty in domain adaptation for mri prostate lesion segmentation
%P 510-520
%X The need for training data can impede the adoption of novel imaging modalities for learning-based medical image analysis. Domain adaptation methods partially mitigate this problem by translating training data from a related source domain to a novel target domain, but typically assume that a one-to-one translation is possible. Our work addresses the challenge of adapting to a more informative target domain where multiple target samples can emerge from a single source sample. In particular we consider translating from mp-MRI to VERDICT, a richer MRI modality involving an optimized acquisition protocol for cancer characterization. We explicitly account for the inherent uncertainty of this mapping and exploit it to generate multiple outputs conditioned on a single input. Our results show that this allows us to extract systematically better image representations for the target domain, when used in tandem with both simple, CycleGAN-based baselines, as well as more powerful approaches that integrate discriminative segmentation losses and/or residual adapters. When compared to its deterministic counterparts, our approach yields substantial improvements across a broad range of dataset sizes, increasingly strong baselines, and evaluation measures.
%J Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
%A E Chiou
%A F Giganti
%A S Punwani
%A I Kokkinos
%A E Panagiotaki
%O This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
%I Springer
%E A.L Martel
%E P Abolmaesumi
%E D Stoyanov
%E D Mateus
%E M.A Zuluaga
%E S.K Zhou
%E D Racoceanu
%E L Joskowicz
%L discovery10115471