MRI-Based Deep Learning for Automatic Segmentation of Punctate White Matter Injury in Neonates

Motivation: Punctate white matter injury (PWMI) in neonates is characterized by small lesions and significant sample variability, posing a challenge for quantification. Goal(s): We introduce a novel approach that combines the 3D nnU-Net framework for semantic segmentation of PWMI using neonatal brain MR images. Approach: The PWML automatic segmentation models, based on 3D-T1WI, was developed utilizing V-Net, VB-Net, 2D nnU-Net and 3D nnU-Net. Automatic localization of lesions and quantitative analysis of the brain regions were further realized by segmentation of dHCP template brain regions. Results: The automatic segmentation model demonstrated robust performance, achieving a median Dice Similarity Coefficientn of 0.865 on the test set. Impact: This innovation offers an automatic and accurate segmentation of PWMI regions, potentially providing clinicians with a powerful tool for the automatic localization and classification model construction, quantitative analysis and grading prognostic study of PWMI in neonates.