PARADISE: Personalized and regional adaptation for HIE disease identification and segmentation

Abstract

Hypoxic ischemic encephalopathy (HIE) is a brain dysfunction occurring in approximately 1-5/1000 term-born neonates. Accurate segmentation of HIE lesions in brain MRI is crucial for prognosis and diagnosis but presents a unique challenge due to the diffuse and small nature of these abnormalities, which resulted in a substantial gap between the performance of machine learning-based segmentation methods and clinical expert annotations for HIE. To address this challenge, we introduce ParadiseNet, an algorithm specifically designed for HIE lesion segmentation. ParadiseNet incorporates global–local learning, progressive uncertainty learning, and self-evolution learning modules, all inspired by clinical interpretation of neonatal brain MRIs. These modules target issues such as unbalanced data distribution, boundary uncertainty, and imprecise lesion detection, respectively. Extensive experiments demonstrate that ParadiseNet significantly enhances small lesion detection ($<1\text{\%}$) accuracy in HIE, achieving an over 4% improvement in Dice, 6% improvement in NSD compared to U-Net and other general medical image segmentation algorithms.