具有微观分辨率的主嗅球解剖结构交互式图像分割方法

IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Xin Liu, Anan Li, Yue Luo, Shengda Bao, Tao Jiang, Xiangning Li, Jing Yuan, Zhao Feng
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引用次数: 0

摘要

主嗅球是啮齿动物嗅觉通路的关键部分。要精确解剖主嗅球(MOB)的神经通路,就必须以微观分辨率构建其内部解剖结构的三维形态。然而,由于主嗅球的解剖结构形状复杂,而且显微光学图像的分辨率较高,因此构建三维形态仍然具有挑战性。为了解决这些问题,我们提出了一种在水平和轴向具有微米级分辨率的交互式体积图像分割方法。首先,我们通过人工标注获得解剖结构的初始位置,并设计一个基于补丁的神经网络来学习解剖结构的复杂纹理特征。然后,我们通过训练有素的网络随机抽取一些斑块进行预测,并根据强度计算进行标注重建,从而得到解剖结构的最终定位结果。我们利用微光学切片断层扫描(MOST)系统获取的尼氏染色脑图像进行了实验。我们的方法取得了 81.8% 的平均骰子相似系数(DSC),获得了最佳的分割性能。同时,实验表明主嗅球解剖结构的三维形态重建结果是平滑的,并且与它们的自然形状一致,这为构建全脑解剖结构的三维形态提供了可能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An interactive image segmentation method for the anatomical structures of the main olfactory bulb with micro-level resolution
The main olfactory bulb is the key element of the olfactory pathway of rodents. To precisely dissect the neural pathway in the main olfactory bulb (MOB), it is necessary to construct the three-dimensional morphologies of the anatomical structures within it with micro-level resolution. However, the construction remains challenging due to the complicated shape of the anatomical structures in the main olfactory bulb and the high resolution of micro-optical images. To address these issues, we propose an interactive volume image segmentation method with micro-level resolution in the horizontal and axial direction. Firstly, we obtain the initial location of the anatomical structures by manual annotation and design a patch-based neural network to learn the complex texture feature of the anatomical structures. Then we randomly sample some patches to predict by the trained network and perform an annotation reconstruction based on intensity calculation to get the final location results of the anatomical structures. Our experiments were conducted using Nissl-stained brain images acquired by the Micro-optical sectioning tomography (MOST) system. Our method achieved a mean dice similarity coefficient (DSC) of 81.8% and obtain the best segmentation performance. At the same time, the experiment shows the three-dimensional morphology reconstruction results of the anatomical structures in the main olfactory bulb are smooth and consistent with their natural shapes, which addresses the possibility of constructing three-dimensional morphologies of the anatomical structures in the whole brain.
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来源期刊
Frontiers in Neuroinformatics
Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
4.80
自引率
5.70%
发文量
132
审稿时长
14 weeks
期刊介绍: Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states. Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.
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