3D Masked Autoencoding and Pseudo-labeling for Domain Adaptive Segmentation of Heterogeneous Infant Brain MRI
Xuzhe ZhangYuhao WuJia GuoJerod M. RasmussenThomas G. O'ConnorHyagriv N. SimhanSonja EntringerPathik D. WadhwaClaudia BussCristiane S. DuarteAndrea JackowskiHai LiJonathan PosnerAndrew F. LaineYun Wang
Xuzhe ZhangYuhao WuJia Guo
...+11
Yun Wang
Mar 2023
0被引用
1笔记
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摘要原文
Robust segmentation of infant brain MRI across multiple ages, modalities, andsites remains challenging due to the intrinsic heterogeneity caused bydifferent MRI scanners, vendors, or acquisition sequences, as well as varyingstages of neurodevelopment. To address this challenge, previous studies haveexplored domain adaptation (DA) algorithms from various perspectives, includingfeature alignment, entropy minimization, contrast synthesis (style transfer),and pseudo-labeling. This paper introduces a novel framework called MAPSeg(Masked Autoencoding and Pseudo-labelling Segmentation) to address thechallenges of cross-age, cross-modality, and cross-site segmentation ofsubcortical regions in infant brain MRI. Utilizing 3D masked autoencoding aswell as masked pseudo-labeling, the model is able to jointly learn from labeledsource domain data and unlabeled target domain data. We evaluated our frameworkon expert-annotated datasets acquired from different ages and sites. MAPSegconsistently outperformed other methods, including previous state-of-the-artsupervised baselines, domain generalization, and domain adaptation frameworksin segmenting subcortical regions regardless of age, modality, or acquisitionsite. The code and pretrained encoder will be publicly available athttps://github.com/XuzheZ/MAPSeg
