Enhancing Generalized Fetal Brain MRI Segmentation using A Cascade Network with Depth-wise Separable Convolution and Attention Mechanism

TL;DR

This paper introduces CasUNext, a cascade network with attention and depth-wise separable convolutions, significantly improving fetal brain MRI segmentation accuracy and generalization across diverse datasets and abnormal cases.

Contribution

The novel CasUNext architecture combines cascade design, attention mechanisms, and depth-wise separable convolutions to enhance fetal brain MRI segmentation performance.

Findings

Achieved an average Dice coefficient of 96.1%

Outperformed U-Nets and state-of-the-art methods

Effective on multi-view and abnormal fetal MRI data

Abstract

Automatic segmentation of the fetal brain is still challenging due to the health state of fetal development, motion artifacts, and variability across gestational ages, since existing methods rely on high-quality datasets of healthy fetuses. In this work, we propose a novel cascade network called CasUNext to enhance the accuracy and generalization of fetal brain MRI segmentation. CasUNext incorporates depth-wise separable convolution, attention mechanisms, and a two-step cascade architecture for efficient high-precision segmentation. The first network localizes the fetal brain region, while the second network focuses on detailed segmentation. We evaluate CasUNext on 150 fetal MRI scans between 20 to 36 weeks from two scanners made by Philips and Siemens including axial, coronal, and sagittal views, and also validated on a dataset of 50 abnormal fetuses. Results demonstrate that CasUNext achieves improved segmentation performance compared to U-Nets and other state-of-the-art approaches. It obtains an average Dice coefficient of 96.1% and mean intersection over union of 95.9% across diverse scenarios. CasUNext shows promising capabilities for handling the challenges of multi-view fetal MRI and abnormal cases, which could facilitate various quantitative analyses and apply to multi-site data.