FreeHemoSeg: Label-Free Deep Learning Framework for Automated Segmentation of Fetal Brain Germinal Matrix and Intraventricular Hemorrhage

Motivation: Antenatal GMH-IVH is a significant cause of infant mortality and morbidity. Deep learning model for automatically diagnosing GMH-IVH requires a large amount of brain data with GMH-IVH and labels for training, which are difficult to obtain due to the rarity of GMH-IVH. Goal(s): Deep learning model that can be simply trained on data from healthy subjects for segmenting GMH-IVH. Approach: FreeHemoSeg was proposed to synthesize pseudo slices with GMH-IVH from normal images for training a neural network. Results: FreeHemoSeg exhibited superior diagnostic and segmentation accuracy, outperforming unsupervised methods and networks trained on limited labeled data from patients. Impact: FreeHemoSeg provides accurate, automated segmentation and diagnosis of GMH-IVH without hemorrhage data and labels for training, substantially simplifying clinical workflows, aiding early diagnosis and prognosis, enabling hemorrhage volume measurement, supporting large-scale neuroscience research, and enhancing prenatal care and management strategies.