  Quantifying segmentation uncertainty has become an important issue in medical
image analysis due to the inherent ambiguity of anatomical structures and its
pathologies. Recently, neural network-based uncertainty quantification methods
have been successfully applied to various problems. One of the main limitations
of the existing techniques is the high memory requirement during training;
which limits their application to processing smaller field-of-views (FOVs)
and/or using shallower architectures. In this paper, we investigate the effect
of using reversible blocks for building memory-efficient neural network
architectures for quantification of segmentation uncertainty. The reversible
architecture achieves memory saving by exactly computing the activations from
the outputs of the subsequent layers during backpropagation instead of storing
the activations for each layer. We incorporate the reversible blocks into a
recently proposed architecture called PHiSeg that is developed for uncertainty
quantification in medical image segmentation. The reversible architecture,
RevPHiSeg, allows training neural networks for quantifying segmentation
uncertainty on GPUs with limited memory and processing larger FOVs. We perform
experiments on the LIDC-IDRI dataset and an in-house prostate dataset, and
present comparisons with PHiSeg. The results demonstrate that RevPHiSeg
consumes ~30% less memory compared to PHiSeg while achieving very similar
segmentation accuracy.
