How Reliable Are Out-of-Distribution Generalization Methods for Medical Image Segmentation?

Abstract

The recent achievements of Deep Learning rely on the test data being similar in distribution to the training data. In an ideal case, Deep Learning models would achieve Out-of-Distribution (OoD) Generalization, i.e. reliably make predictions on out-of-distribution data. Yet in practice, models usually fail to generalize well when facing a shift in distribution. Several methods were thereby designed to improve the robustness of the features learned by a model through Regularizationor Domain-Prediction-based schemes. Segmenting medical images such as MRIs of the hippocampus is essential for the diagnosis and treatment of neuropsychiatric disorders. But these brain images often suffer from distribution shift due to the patient's age and various pathologies affecting the shape of the organ. In this work, we evaluate OoD Generalization solutions for the problem of hippocampus segmentation in MR data using both fully-and semi-supervised training. We find that no method performs reliably in all experiments. Only the V-REx loss stands out as it remains easy to tune, while it outperforms a standard U-Net in most cases.

MiniVess: A dataset of rodent cerebrovasculature from in vivo multiphoton fluorescence microscopy imaging

“…We hereby present MiniVess, an expert-annotated dataset of 70 3D 2PFM image volumes of rodent cerebrovasculature. The dataset can be used for training segmentation networks 38,39 , fine-tuning self-supervised pre-trained networks 31,32,40 , and as an external validation set for assessing a model's generalizability 41 . The 3D volumes in this dataset have been curated to only contain clean XYZ imaging in order to ensure correct and consistent annotations, or segmentations, which has been observed to be integral to the evaluation of machine learning models 42 .…”