基于解耦学习、对运动伪影鲁棒的形变感知脑组织分割网络
Deformation-Aware Segmentation Network Robust to Motion Artifacts for Brain Tissue Segmentation using Disentanglement Learning
AI总结：
本文提出结合解耦学习、运动估计与分割的形变感知框架，通过运动形变图引导伪影去除，显著提升受运动伪影影响的脑组织MRI分割性能。
摘要：
由较长采集时间引起的运动伪影是磁共振成像（MRI）中的一项重大挑战，会妨碍准确的组织分割。这些伪影表现为模糊图像，并呈现类似组织的外观，使分割变得困难。本研究提出了一种新颖的深度学习框架，在运动校正以及存在伪影时的鲁棒脑组织分割两方面均表现出优越性能。其核心概念在于一个互补过程：解耦学习网络逐步去除伪影，得到更干净的图像，进而由联合训练的运动估计与分割网络实现更准确的分割。该网络生成三个输出：运动校正图像、用于识别受伪影影响区域的运动形变图，以及脑组织分割掩膜。该形变作为解耦过程的引导机制，帮助模型恢复丢失的信息或去除伪影引入的人工结构。在儿童运动数据上进行的大量在体实验表明，所提出框架在分割受运动损坏的MRI扫描方面优于现有最先进方法。
英文摘要：
Motion artifacts caused by prolonged acquisition time are a significant challenge in Magnetic Resonance Imaging (MRI), hindering accurate tissue segmentation. These artifacts appear as blurred images that mimic tissue-like appearances, making segmentation difficult. This study proposes a novel deep learning framework that demonstrates superior performance in both motion correction and robust brain tissue segmentation in the presence of artifacts. The core concept lies in a complementary process: a disentanglement learning network progressively removes artifacts, leading to cleaner images and consequently, more accurate segmentation by a jointly trained motion estimation and segmentation network. This network generates three outputs: a motioncorrected image, a motion deformation map that identifies artifact-affected regions, and a brain tissue segmentation mask. This deformation serves as a guidance mechanism for the disentanglement process, aiding the model in recovering lost information or removing artificial structures introduced by the artifacts. Extensive in-vivo experiments on pediatric motion data demonstrate that our proposed framework outperforms state-of-the-art methods in segmenting motion-corrupted MRI scans.
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