arXiv 2303.093732024-03-30cs.CVcs.AIcs.LG

MAPSeg：基于3D掩码自编码与伪标签的统一无监督域自适应异构医学图像分割

MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling

Columbia University（哥伦比亚大学）；Télécom Paris（巴黎电信学院）；Institut Polytechnique de Paris（巴黎综合理工学院）；University of Maryland, College Park（马里兰大学帕克分校）；University of California, Irvine（加州大学欧文分校）；University of Rochester（罗切斯特大学）；Universidade Federal de São Paulo（圣保罗联邦大学）；Duke University（杜克大学）；Emory University（埃默里大学）

Xuzhe Zhang, Yuhao Wu, Elsa Angelini, Ang Li, Jia Guo, Jerod M. Rasmussen, Thomas G. O'Connor, Pathik D. Wadhwa, Andrea Parolin Jackowski, Hai Li, Jonathan Posn… 展开作者

Xuzhe Zhang, Yuhao Wu, Elsa Angelini, Ang Li, Jia Guo, Jerod M. Rasmussen, Thomas G. O'Connor, Pathik D. Wadhwa, Andrea Parolin Jackowski, Hai Li, Jonathan Posner, Andrew F. Laine, Yun Wang

备注：: CVPR 2024 camera-ready (8 pages, 3 figures) with the supplemental materials (5 pages, 4 figures). Xuzhe Zhang and Yuhao Wu are co-first authors. Andrew F. Laine and Yun Wang are co-senior supervising authors

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AI总结：

本文提出统一无监督域自适应框架MAPSeg，结合3D掩码自编码与伪标签，首次同时支持集中式、联邦式和测试时UDA，并在婴儿脑MRI和心脏CT-MRI分割上显著超越现有方法。

摘要：

鲁棒分割对于从大规模、多中心和纵向医学扫描中获取定量指标至关重要。然而，手动标注医学扫描既昂贵又费力，并且并非在每个域中都能获得标注。无监督域自适应（UDA）是一项经过充分研究的技术，通过利用来自另一个域的可用标签来缓解标签稀缺问题。在本研究中，我们提出了掩码自编码与伪标签分割（MAPSeg），这是一个统一的UDA框架，具有高度通用性和优越性能，适用于异构和三维医学图像分割。据我们所知，这是首个系统回顾并开发框架以应对医学图像分割中四种不同域偏移的研究。更重要的是，MAPSeg是第一个能够应用于集中式、联邦式和测试时UDA，同时保持可比性能的框架。我们在一个私有婴儿脑MRI数据集和一个公开心脏CT-MRI数据集上将MAPSeg与先前最先进的方法进行了比较，MAPSeg大幅优于其他方法（在私有MRI数据集上Dice提升10.5，在公开CT-MRI数据集上提升5.7）。MAPSeg具有很高的实用价值，可应用于现实世界问题。GitHub：https://github.com/XuzheZ/MAPSeg/。

英文摘要：

Robust segmentation is critical for deriving quantitative measures from large-scale, multi-center, and longitudinal medical scans. Manually annotating medical scans, however, is expensive and labor-intensive and may not always be available in every domain. Unsupervised domain adaptation (UDA) is a well-studied technique that alleviates this label-scarcity problem by leveraging available labels from another domain. In this study, we introduce Masked Autoencoding and Pseudo-Labeling Segmentation (MAPSeg), a $\textbf{unified}$ UDA framework with great versatility and superior performance for heterogeneous and volumetric medical image segmentation. To the best of our knowledge, this is the first study that systematically reviews and develops a framework to tackle four different domain shifts in medical image segmentation. More importantly, MAPSeg is the first framework that can be applied to $\textbf{centralized}$, $\textbf{federated}$, and $\textbf{test-time}$ UDA while maintaining comparable performance. We compare MAPSeg with previous state-of-the-art methods on a private infant brain MRI dataset and a public cardiac CT-MRI dataset, and MAPSeg outperforms others by a large margin (10.5 Dice improvement on the private MRI dataset and 5.7 on the public CT-MRI dataset). MAPSeg poses great practical value and can be applied to real-world problems. GitHub: https://github.com/XuzheZ/MAPSeg/.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。