Abstract
Ex vivo MRI of the brain provides remarkable advantages over in vivo MRI for visualizing and characterizing detailed neuroanatomy, and helps to link microscale histology studies with morphometric measurements. However, automated segmentation methods for brain mapping in ex vivo MRI are not well developed, primarily due to limited availability of labeled datasets, and heterogeneity in scanner hardware and acquisition protocols. In this work, we present a high resolution dataset of 37 ex vivo post-mortem human brain tissue specimens scanned on a 7T whole-body MRI scanner. We developed a deep learning pipeline to segment the cortical mantle by benchmarking the performance of nine deep neural architectures. We then segment the four subcortical structures: caudate, putamen, globus pallidus, and thalamus; white matter hyperintensities, and the normal appearing white matter. We show excellent generalizing capabilities across whole brain hemispheres in different specimens, and also on unseen images acquired at different magnetic field strengths and different imaging sequence. We then compute volumetric and localized cortical thickness measurements across key regions, and link them with semi-quantitative neuropathological ratings. Our code, containerized executables, and the processed datasets are publicly available at: this https URL.
Abstract (translated)
体外MRI扫描大脑提供了比体内MRI对于可视化和描述详细神经解剖的显著优势,并有助于将微尺度脑组织学研究与形态学测量连接起来。然而,在体外MRI扫描中的大脑映射方法并没有得到很好的发展,主要是因为标记数据数量有限,以及扫描设备硬件和采集协议的不一致性。在本研究中,我们提出了一个高分辨率的数据集,包括37个体外离体人类脑组织样本扫描,使用7T全身MRI扫描器进行。我们开发了一道深度学习管道,以基准九种深度学习架构的性能,将 cortical mantle 分段。然后我们分段了四个底结构:奇偶性脑叶、扣带回、脑白质和脑灰质;脑浆过载和正常表现的脑白质。在不同样本中,我们展示了在不同脑半球整个大脑的泛化能力,以及在不同磁场强度和图像序列下获得的未观察过的图像。然后我们计算了关键区域的体积化和定位的cortical厚度测量,并将它们与半定量神经病理学评级连接起来。我们的代码、容器化可执行文件和处理的数据集是公开可用的,在此 https URL 上。
URL
https://arxiv.org/abs/2303.12237
