论文标题
先天性或脑解剖学扭曲的患者的自动组织分割，并进行深度学习
Automatic Tissue Segmentation with Deep Learning in Patients with Congenital or Acquired Distortion of Brain Anatomy
论文作者
论文摘要
大脑解剖结构变形的大脑对T1加权MR图像的自动组织分割方法提出了一些挑战。首先，组织形态的极高可变性与嵌入算法中的先验知识不相容。其次，扭曲大脑的MR图像的可用性非常稀缺，因此文献中的方法尚未解决此类情况。在这项工作中，我们介绍了对先天性或获得性脑部变形的大脑的T1加权图像的最新自动组织分割管道的首次评估。我们比较了传统的管道和深度学习模型，即接受正常表现大脑的3D U-NET。毫不奇怪，传统管道完全无法用强烈的解剖变形分段组织。令人惊讶的是，3D U-NET提供了有用的分割，这可能是专家/神经放射学家手动改进的宝贵起点。
Brains with complex distortion of cerebral anatomy present several challenges to automatic tissue segmentation methods of T1-weighted MR images. First, the very high variability in the morphology of the tissues can be incompatible with the prior knowledge embedded within the algorithms. Second, the availability of MR images of distorted brains is very scarce, so the methods in the literature have not addressed such cases so far. In this work, we present the first evaluation of state-of-the-art automatic tissue segmentation pipelines on T1-weighted images of brains with different severity of congenital or acquired brain distortion. We compare traditional pipelines and a deep learning model, i.e. a 3D U-Net trained on normal-appearing brains. Unsurprisingly, traditional pipelines completely fail to segment the tissues with strong anatomical distortion. Surprisingly, the 3D U-Net provides useful segmentations that can be a valuable starting point for manual refinement by experts/neuroradiologists.
