This website requires JavaScript.

3D Masked Autoencoding and Pseudo-labeling for Domain Adaptive Segmentation of Heterogeneous Infant Brain MRI

Xuzhe ZhangYuhao WuJia Guo ...+11 Yun Wang
Mar 2023
摘要
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
展开全部
图表提取

暂无人提供速读十问回答

论文十问由沈向洋博士提出,鼓励大家带着这十个问题去阅读论文,用有用的信息构建认知模型。写出自己的十问回答,还有机会在当前页面展示哦。

Q1论文试图解决什么问题?
Q2这是否是一个新的问题?
Q3这篇文章要验证一个什么科学假设?