One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data
One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data
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DOI：
10.1145/3580305.3599452
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发表时间：
2023-07
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影响因子：
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通讯作者：
Yao Su;Zhentian Qian;Lei Ma;Lifang He;Xiangnan Kong
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文献类型：
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作者：
Yao Su;Zhentian Qian;Lei Ma;Lifang He;Xiangnan Kong
脑提取、配准和分割是神经影像学研究中不可缺少的预处理步骤。目的是从原始成像扫描中提取大脑（即提取步骤），将其与目标大脑图像对齐（即配准步骤）并标记大脑解剖区域（即分割步骤）。传统的研究通常侧重于开发单独的方法来完成在监督环境下的提取、注册和分割任务。这些方法的性能在很大程度上取决于训练样本的数量和专家为纠错而进行的目视检查的程度。然而，在许多医学研究中，收集体素级标签并对高维神经图像（如3D MRI）进行人工质量控制既昂贵又耗时。本文研究了神经成像数据的一次联合提取、配准和分割问题，该问题仅利用一张有标记的模板图像（即地图集）和少量未标记的原始图像进行训练。我们提出了一个统一的端到端框架，称为JERS，来共同优化提取、配准和分割任务，并允许它们之间的反馈。具体来说，我们使用一组提取、配准和分割模块来学习提取掩码、变换掩码和分割掩码，其中模块之间相互联系，通过自我监督相互加强。在实际数据集上的实验结果表明，我们提出的方法在提取、配准和分割任务中表现优异。
Brain extraction, registration and segmentation are indispensable preprocessing steps in neuroimaging studies. The aim is to extract the brain from raw imaging scans (i.e., extraction step), align it with a target brain image (i.e., registration step) and label the anatomical brain regions (i.e., segmentation step). Conventional studies typically focus on developing separate methods for the extraction, registration and segmentation tasks in a supervised setting. The performance of these methods is largely contingent on the quantity of training samples and the extent of visual inspections carried out by experts for error correction. Nevertheless, collecting voxel-level labels and performing manual quality control on high-dimensional neuroimages (e.g., 3D MRI) are expensive and time-consuming in many medical studies. In this paper, we study the problem of one-shot joint extraction, registration and segmentation in neuroimaging data, which exploits only one labeled template image (a.k.a. atlas) and a few unlabeled raw images for training. We propose a unified end-to-end framework, called JERS, to jointly optimize the extraction, registration and segmentation tasks, allowing feedback among them. Specifically, we use a group of extraction, registration and segmentation modules to learn the extraction mask, transformation and segmentation mask, where modules are interconnected and mutually reinforced by self-supervision. Empirical results on real-world datasets demonstrate that our proposed method performs exceptionally in the extraction, registration and segmentation tasks.