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 this https URL

本文研究了神经影像数据中的一次性联合提取、配准和分割问题，提出了一种统一的端到端框架 JERS，它利用一个标记的模板图像和一些未标记的原始图像进行训练，通过自监督相互增强来优化提取、配准和分割任务。实证结果表明，我们提出的方法在提取、配准和分割任务中表现出色。