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Abstract #3790

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

Mingxuan Liu1, Yi Liao2, Juncheng Zhu2, Haoxiang Li1, Hongjia Yang1, Jialan Zheng1,3, Zihan Li1, Ziyu Li4, Haibo Qu2, and Qiyuan Tian1,5
1School of Biomedical Engineering, Tsinghua University, Beijing, China, 2West China Second University Hospital, Chengdu, China, 3Tanwei College, Tsinghua University, Beijing, China, 4Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, 5Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China

Synopsis

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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Keywords