  The performance of deep neural networks typically increases with the number
of training images. However, not all images have the same importance towards
improved performance and robustness. In fetal brain MRI, abnormalities
exacerbate the variability of the developing brain anatomy compared to
non-pathological cases. A small number of abnormal cases, as is typically
available in clinical datasets used for training, are unlikely to fairly
represent the rich variability of abnormal developing brains. This leads
machine learning systems trained by maximizing the average performance to be
biased toward non-pathological cases. This problem was recently referred to as
hidden stratification. To be suited for clinical use, automatic segmentation
methods need to reliably achieve high-quality segmentation outcomes also for
pathological cases. In this paper, we show that the state-of-the-art deep
learning pipeline nnU-Net has difficulties to generalize to unseen abnormal
cases. To mitigate this problem, we propose to train a deep neural network to
minimize a percentile of the distribution of per-volume loss over the dataset.
We show that this can be achieved by using Distributionally Robust Optimization
(DRO). DRO automatically reweights the training samples with lower performance,
encouraging nnU-Net to perform more consistently on all cases. We validated our
approach using a dataset of 368 fetal brain T2w MRIs, including 124 MRIs of
open spina bifida cases and 51 MRIs of cases with other severe abnormalities of
brain development.
