Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK.
Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK; Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, USA.
Neuroimage. 2022 Nov;263:119616. doi: 10.1016/j.neuroimage.2022.119616. Epub 2022 Sep 6.
This paper reviews almost three decades of work on atlasing and segmentation methods for subcortical structures in human brain MRI. In writing this survey, we have three distinct aims. First, to document the evolution of digital subcortical atlases of the human brain, from the early MRI templates published in the nineties, to the complex multi-modal atlases at the subregion level that are available today. Second, to provide a detailed record of related efforts in the automated segmentation front, from earlier atlas-based methods to modern machine learning approaches. And third, to present a perspective on the future of high-resolution atlasing and segmentation of subcortical structures in in vivo human brain MRI, including open challenges and opportunities created by recent developments in machine learning.
本文回顾了近三十年在人类大脑 MRI 的皮质下结构图谱和分割方法方面的工作。在撰写这篇综述时，我们有三个明确的目标。首先，记录人脑的数字化皮质下图谱的演变过程，从 90 年代早期发表的早期 MRI 模板，到如今可用的复杂的多模态亚区图谱。其次，详细记录自动分割方面的相关工作，从早期基于图谱的方法到现代机器学习方法。第三，对未来活体人脑 MRI 中皮质下结构的高分辨率图谱和分割提出展望，包括机器学习最新进展所带来的挑战和机遇。
