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. Our code and data can be found at https://github.com/Anonymous4545/JERS

翻译：脑提取、配准与分割是神经影像研究中不可或缺的预处理步骤。其目标是从原始影像扫描中提取脑区（即提取步骤）、将其与目标脑影像对齐（即配准步骤）并标注解剖脑区（即分割步骤）。传统研究通常侧重于在监督设置下针对提取、配准和分割任务分别开发不同方法。这些方法的性能在很大程度上取决于训练样本的数量以及专家进行误差校正的视觉检查工作量。然而，在众多医学研究中，收集体素级标签并对高维神经影像（例如3D MRI）进行手动质量控制既昂贵又耗时。本文研究神经影像数据中一次性联合提取、配准与分割的问题，该方法仅利用一个带标签的模板图像（亦称图谱）和少量未标注的原始图像进行训练。我们提出一个名为JERS的统一端到端框架，用于联合优化提取、配准与分割任务，允许它们之间相互反馈。具体而言，我们使用一组提取、配准和分割模块来学习提取掩膜、变换和分割掩膜，这些模块通过自监督相互连接并相互增强。在真实世界数据集上的实验结果表明，我们所提出的方法在提取、配准与分割任务中表现优异。我们的代码和数据可在https://github.com/Anonymous4545/JERS获取。