A Study of Demographic Bias in CNN-based Brain MR Segmentation

TL;DR

This study investigates demographic biases in CNN-based brain MRI segmentation, revealing significant sex and race biases, especially racial bias, influenced by training data imbalance, which can impact health research fairness.

Contribution

It demonstrates the presence of sex and race biases in CNN brain segmentation models and emphasizes the importance of balanced training data to prevent health disparities.

Findings

Race bias is more significant than sex bias.

Bias varies across different brain regions.

Imbalanced training sets exacerbate demographic biases.

Abstract

Convolutional neural networks (CNNs) are increasingly being used to automate the segmentation of brain structures in magnetic resonance (MR) images for research studies. In other applications, CNN models have been shown to exhibit bias against certain demographic groups when they are under-represented in the training sets. In this work, we investigate whether CNN models for brain MR segmentation have the potential to contain sex or race bias when trained with imbalanced training sets. We train multiple instances of the FastSurferCNN model using different levels of sex imbalance in white subjects. We evaluate the performance of these models separately for white male and white female test sets to assess sex bias, and furthermore evaluate them on black male and black female test sets to assess potential racial bias. We find significant sex and race bias effects in segmentation model performance. The biases have a strong spatial component, with some brain regions exhibiting much stronger bias than others. Overall, our results suggest that race bias is more significant than sex bias. Our study demonstrates the importance of considering race and sex balance when forming training sets for CNN-based brain MR segmentation, to avoid maintaining or even exacerbating existing health inequalities through biased research study findings.