A Deep Learning-based Pipeline for Segmenting the Cerebral Cortex Laminar Structure in Histology Images.

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Neuroinformatics Pub Date : 2024-10-01 Epub Date: 2024-10-17 DOI:10.1007/s12021-024-09688-0
Jiaxuan Wang, Rui Gong, Shahrokh Heidari, Mitchell Rogers, Toshiki Tani, Hiroshi Abe, Noritaka Ichinohe, Alexander Woodward, Patrice J Delmas
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引用次数: 0

Abstract

Characterizing the anatomical structure and connectivity between cortical regions is a critical step towards understanding the information processing properties of the brain and will help provide insight into the nature of neurological disorders. A key feature of the mammalian cerebral cortex is its laminar structure. Identifying these layers in neuroimaging data is important for understanding their global structure and to help understand the connectivity patterns of neurons in the brain. We studied Nissl-stained and myelin-stained slice images of the brain of the common marmoset (Callithrix jacchus), which is a new world monkey that is becoming increasingly popular in the neuroscience community as an object of study. We present a novel computational framework that first acquired the cortical labels using AI-based tools followed by a trained deep learning model to segment cerebral cortical layers. We obtained a Euclidean distance of 1274.750 ± 156.400 μ m for the cortical labels acquisition, which was in the acceptable range by computing the half Euclidean distance of the average cortex thickness ( 1800.630 μ m ). We compared our cortical layer segmentation pipeline with the pipeline proposed by Wagstyl et al. (PLoS biology, 18(4), e3000678 2020) adapted to 2D data. We obtained a better mean 95 th percentile Hausdorff distance (95HD) of  92.150 μ m . Whereas a mean 95HD of  94.170 μ m was obtained from Wagstyl et al. We also compared our pipeline's performance against theirs using their dataset (the BigBrain dataset). The results also showed better segmentation quality, 85.318 % Jaccard Index acquired from our pipeline, while 83.000 % was stated in their paper.

基于深度学习的管道,用于分割组织学图像中的大脑皮层层状结构。
描述大脑皮层区域之间的解剖结构和连通性是了解大脑信息处理特性的关键一步,有助于深入了解神经系统疾病的本质。哺乳动物大脑皮层的一个主要特征是层状结构。从神经影像数据中识别这些层对于了解其整体结构和帮助理解大脑神经元的连接模式非常重要。我们研究了普通狨猴(Callithrix jacchus)大脑的尼氏染色和髓鞘染色切片图像。我们提出了一个新颖的计算框架,首先使用基于人工智能的工具获取皮层标签,然后使用训练有素的深度学习模型分割大脑皮层。通过计算平均皮层厚度的一半欧氏距离(1800.630 μ m),我们得出皮层标签获取的欧氏距离为 1274.750 ± 156.400 μ m,在可接受范围内。我们将皮质层分割管道与 Wagstyl 等人提出的适用于二维数据的管道(PLoS biology, 18(4), e3000678 2020)进行了比较。我们获得了更好的平均 95th 百分位数豪斯多夫距离(95HD),为 92.150 μ m。我们还使用 Wagstyl 等人的数据集(BigBrain 数据集)与他们的数据集进行了比较。结果也显示了更好的分割质量,我们的管道获得了 85.318 % 的 Jaccard 指数,而他们的论文中提到的是 83.000 %。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
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