Keywords: Fetal, Fetal, Brain, GMH-IVH, Rare disease
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.
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