Bilateral Hippocampi Segmentation in Low Field MRIs Using Mutual Feature Learning via Dual-Views

TL;DR

This paper introduces a novel deep-learning method for accurate bilateral hippocampi segmentation in low-field MRIs, enhancing accessibility and diagnostic capabilities in resource-limited settings.

Contribution

The proposed dual-view model employs mutual feature learning with high-frequency masking to improve segmentation accuracy in low-quality MRI images.

Findings

Achieves reliable hippocampal segmentation in low-field MRIs

Outperforms existing methods in accuracy and robustness

Code is publicly available for reproducibility

Abstract

Accurate hippocampus segmentation in brain MRI is critical for studying cognitive and memory functions and diagnosing neurodevelopmental disorders. While high-field MRIs provide detailed imaging, low-field MRIs are more accessible and cost-effective, which eliminates the need for sedation in children, though they often suffer from lower image quality. In this paper, we present a novel deep-learning approach for the automatic segmentation of bilateral hippocampi in low-field MRIs. Extending recent advancements in infant brain segmentation to underserved communities through the use of low-field MRIs ensures broader access to essential diagnostic tools, thereby supporting better healthcare outcomes for all children. Inspired by our previous work, Co-BioNet, the proposed model employs a dual-view structure to enable mutual feature learning via high-frequency masking, enhancing segmentation accuracy by leveraging complementary information from different perspectives. Extensive experiments demonstrate that our method provides reliable segmentation outcomes for hippocampal analysis in low-resource settings. The code is publicly available at: https://github.com/himashi92/LoFiHippSeg.