Spatial Prior-Guided Boundary and Region-Aware 2D Lesion Segmentation in Neonatal Hypoxic Ischemic Encephalopathy

Abstract

Segmenting acute and hyper-acute brain lesions in neona-tal hypoxic-ischemic encephalopathy (HIE) from diffusion-weighted MRI (DWI) is critical for prognosis and treatment planning but remains challenging due to severe class imbalance and lesion variability. We propose a computationally efficient 2D segmentation framework leveraging ADC and ZADC maps as a three-channel input to UNet++ with an Inception-v4 encoder and scSE attention for enhanced spatial-channel recalibration. To address class critical imbalance and lack of volumetric context in 2D methods, we introduce a novel boundary-and-region-aware weighted loss integrating Tversky, Log-Hausdorff, and Focal losses. Our method surpasses state-of-the-art 2D approaches and achieves competitive performance against computationally intensive 3D architectures, securing a Dice Similarity Coefficient (DSC) of 0.6060, Mean Average Surface Distance (MASD) of 2.6484, and Normalized Surface Distance (NSD) of 0.7477. These results establish a new benchmark for neonatal HIE lesion segmentation, demonstrating superior detection of both acute and hyper-acute lesions while mitigating the challenge of loss collapse. The code is available at Neonatal-HIE-SPARSeg.