🤖 AI Summary
Neonatal hypoxic-ischemic encephalopathy (HIE) lesion segmentation in MRI faces challenges including diffuse, multifocal lesions, large inter-lesion volume variability, and severe scarcity of annotated data. To address these, we develop an optimized 3D U-Net framework on the BONBID-HIE dataset and conduct the first systematic evaluation of six loss functions for HIE lesion segmentation. We propose two novel composite losses—Dice-Focal-HausdorffDT and Tversky-HausdorffDT—that jointly optimize regional similarity (via Dice/Tversky/Focal terms) and boundary geometric fidelity (via Hausdorff distance on signed distance transforms). Experimental results demonstrate that Tversky-HausdorffDT achieves the best performance under limited-label settings (Dice = 0.682, Normalized Surface Dice = 0.721), while Dice-Focal-HausdorffDT significantly reduces mean surface distance (1.39 mm). This work establishes a robust, reproducible loss-function design paradigm for HIE lesion segmentation, advancing methodological rigor in neonatal neuroimaging analysis.
📝 Abstract
Segmentation of Hypoxic-Ischemic Encephalopathy (HIE) lesions in neonatal MRI is a crucial but challenging task due to diffuse multifocal lesions with varying volumes and the limited availability of annotated HIE lesion datasets. Using the BONBID-HIE dataset, we implemented a 3D U-Net with optimized preprocessing, augmentation, and training strategies to overcome data constraints. The goal of this study is to identify the optimal loss function specifically for the HIE lesion segmentation task. To this end, we evaluated various loss functions, including Dice, Dice-Focal, Tversky, Hausdorff Distance (HausdorffDT) Loss, and two proposed compound losses -- Dice-Focal-HausdorffDT and Tversky-HausdorffDT -- to enhance segmentation performance. The results show that different loss functions predict distinct segmentation masks, with compound losses outperforming standalone losses. Tversky-HausdorffDT Loss achieves the highest Dice and Normalized Surface Dice scores, while Dice-Focal-HausdorffDT Loss minimizes Mean Surface Distance. This work underscores the significance of task-specific loss function optimization, demonstrating that combining region-based and boundary-aware losses leads to more accurate HIE lesion segmentation, even with limited training data.