Abstract #3788

FetalSFUDA: Source-Free Unsupervised Domain Adaptation for Fetal Brain Extraction from Different Centers or MRI Sequences

Yijin Li¹, Mingxuan Liu², Junchen Zhu³, Hongjia Yang², Jialan Zheng^2,4, Ziyu Li⁵, Yi Liao³, Haibo Qu³, and Qiyuan Tian^2,6

¹School of Biological Science and Medical Engineering, Beihang University, Beijing, China, ²School of Biomedical Engineering, Tsinghua University, Beijing, China, ³Department of Radiology, West China Second University Hospital, Chengdu, China, ⁴Tanwei College, Tsinghua University, Beijing, China, ⁵Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, ⁶Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China

Synopsis

Keywords: Fetal, Fetal, Data Analysis, Data Process, Brain

Motivation: Brain extraction is fundamental in fetal brain MRI 3D reconstruction and analysis. However, the inter-domain generalization of pre-trained deep leaning models is poor since scanner and imaging parameters are diverse among datasets.

Goal(s): Achieve robust fetal brain extraction on out-of-distribution (OOD) target domains using source-free unsupervised domain adaptation (SFUDA) method.

Approach: Parameters of a pre-trained model are optimized for domain adaptation by minimizing a label-free entropy loss and incorporating a class-ratio prior constraint.

Results: The pre-trained model, after applying SFUDA, demonstrates significantly enhanced fetal brain extraction performance on OOD target domains.

Impact: Source-free unsupervised domain adaptation addresses the problem that the pre-trained fetal brain extraction model is inaccurate for data acquired with different scanning hardware and parameters. Moreover, our work supports cross-center fetal studies and promotes practical clinical diagnostic applications.

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