基于深度学习的大脑图像胼胝体分割:综述

IF 1.9 4区 计算机科学 Q3 TELECOMMUNICATIONS Wireless Personal Communications Pub Date : 2024-09-17 DOI:10.1007/s11277-024-11343-5
Padmanabha Sarma, G. Saranya
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引用次数: 0

摘要

大脑中最大的白质结构--胼胝体(CC)与许多中枢神经系统疾病有关。神经退行性疾病的范围和/或严重程度与胼胝体的大小相关。在过去的几个世纪中,虽然有许多方法和程序可以分割胼胝体,而且胼胝体的作用也受到越来越多的关注。然而,现有模型的分割精度并不高。本研究深入分析了用于 CC 分割的各种分割方法。此外,它还研究了不同的深度学习模型,重点关注从脑磁共振成像中获得的 CC 分割。结果表明,在磁共振成像上分割 CC 的计算方法并没有解决所有问题。
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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Deep Learning-Based Corpus Callosum Segmentation from Brain Images: A Review

The largest white matter structure in the brain, the corpus callosum (CC), is involved in many disorders of the central nervous system. The extent and/or severity of neurodegenerative illnesses are correlated with its size. Though numerous approaches and procedures for CC fragmentation have been offered, and the role of CC has been scrutinized more and more over the past few centuries. Nevertheless, the segmentation accuracy of the current models is not very good. This research offers a thorough analysis of various segmentation methods for CC fragmentation. Additionally, it investigates the different deep learning models focused on CC segmentation obtained from brain magnetic resonance imaging. The results show that not all of the issues with the computational methods for segmenting CC on magnetic resonance imaging have been resolved.

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来源期刊
Wireless Personal Communications
Wireless Personal Communications 工程技术-电信学
CiteScore
5.80
自引率
9.10%
发文量
663
审稿时长
6.8 months
期刊介绍: The Journal on Mobile Communication and Computing ... Publishes tutorial, survey, and original research papers addressing mobile communications and computing; Investigates theoretical, engineering, and experimental aspects of radio communications, voice, data, images, and multimedia; Explores propagation, system models, speech and image coding, multiple access techniques, protocols, performance evaluation, radio local area networks, and networking and architectures, etc.; 98% of authors who answered a survey reported that they would definitely publish or probably publish in the journal again. Wireless Personal Communications is an archival, peer reviewed, scientific and technical journal addressing mobile communications and computing. It investigates theoretical, engineering, and experimental aspects of radio communications, voice, data, images, and multimedia. A partial list of topics included in the journal is: propagation, system models, speech and image coding, multiple access techniques, protocols performance evaluation, radio local area networks, and networking and architectures. In addition to the above mentioned areas, the journal also accepts papers that deal with interdisciplinary aspects of wireless communications along with: big data and analytics, business and economy, society, and the environment. The journal features five principal types of papers: full technical papers, short papers, technical aspects of policy and standardization, letters offering new research thoughts and experimental ideas, and invited papers on important and emerging topics authored by renowned experts.
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