Brain extraction and registration are important preprocessing steps in
neuroimaging data analysis, where the goal is to extract the brain regions from
MRI scans (i.e., extraction step) and align them with a target brain image
(i.e., registration step). Conventional research mainly focuses on developing
methods for the extraction and registration tasks separately under supervised
settings. The performance of these methods highly depends on the amount of
training samples and visual inspections performed by experts for error
correction. However, in many medical studies, collecting voxel-level labels and
conducting manual quality control in high-dimensional neuroimages (e.g., 3D
MRI) are very expensive and time-consuming. Moreover, brain extraction and
registration are highly related tasks in neuroimaging data and should be solved
collectively. In this paper, we study the problem of unsupervised collective
extraction and registration in neuroimaging data. We propose a unified
end-to-end framework, called ERNet (Extraction-Registration Network), to
jointly optimize the extraction and registration tasks, allowing feedback
between them. Specifically, we use a pair of multi-stage extraction and
registration modules to learn the extraction mask and transformation, where the
extraction network improves the extraction accuracy incrementally and the
registration network successively warps the extracted image until it is
well-aligned with the target image. Experiment results on real-world datasets
show that our proposed method can effectively improve the performance on
extraction and registration tasks in neuroimaging data. Our code and data can
be found at this https URL

本研究提出了 ERNet 方法来解决神经影像数据中的无监督集成的脑区提取和配准问题，该方法通过一对多级脑区提取和配准模块学习提取掩模和变换，实现迭代提取准确性并成功匹配至目标影像。实验结果表明，ERNet 可以有效地提高神经影像数据处理中的脑区提取和配准任务的性能。