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

TL;DR

This paper evaluates the effectiveness of Out-of-Distribution (OoD) generalization methods for medical image segmentation, specifically hippocampus MRI segmentation, revealing that most methods lack consistent reliability, with V-REx showing promising results.

Contribution

The study provides a comprehensive evaluation of OoD generalization methods in medical image segmentation, highlighting the limited reliability of current approaches and identifying V-REx as a notably effective method.

Findings

Most methods do not perform reliably across all experiments.

V-REx remains easy to tune and outperforms standard U-Net in most cases.

Distribution shifts due to patient age and pathology significantly impact segmentation performance.

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 Regularization- or 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.