自动颞骨分割方法的系统综述

Weijing Li , Sudanthi Wijewickrema , Jan Margeta , Reda Kamraoui , Raabid Hussain , Jean-Marc Gerard
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引用次数: 0

摘要

颞骨是一个复杂的解剖结构,对耳科和神经外科手术至关重要。从计算机断层扫描(CT)和磁共振成像(MRI)中准确分割颞骨对于手术计划、病理评估和计算建模至关重要。人工分割费时且受观察者之间的差异影响,因此需要开发自动化方法。本文综述了目前自动颞骨分割技术的研究现状及其性能。在PubMed、IEEE explore上对2004年至2024年发表的文章进行了全面的搜索。共查阅文献419篇,从中选择34篇纳入本研究。在已确定的研究中,深度学习,特别是卷积神经网络(cnn)和U-Net变体,成为主导方法,始终优于SSM和基于地图集的方法。深度学习模型获得了最高的骰子相似系数(DSC)和最低的豪斯多夫距离(HD)。基于深度学习的方法改进了自动颞骨分割,在分割更大的结构(如迷宫)方面表现出色,Dice评分超过0.86。然而,小解剖结构的分割,如镫骨和鼓室索,仍然是一个挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A systematic review of automated temporal bone segmentation methods
The temporal bone is a complex anatomical structure crucial for otologic and neurotologic procedures. Accurate segmentation of the temporal bone from computed tomography (CT) and magnetic resonance imaging (MRI) is essential for surgical planning, pathology assessment, and computational modeling. Manual segmentation is time-consuming and subject to inter-observer variability, necessitating the development of automated methods. This systematic review aims to analyze the current state of automated temporal bone segmentation techniques and their performance. A comprehensive search was conducted across PubMed, IEEE Xplore for articles published from 2004 to 2024. A total of 419 articles were reviewed, from which 34 were selected for this study. Among the identified studies, deep learning, particularly convolutional neural networks (CNNs) and U-Net variants, emerged as the dominant approach, consistently outperforming SSM and atlas-based methods. Deep learning models achieved the highest Dice Similarity Coefficient (DSC) and the lowest Hausdorff Distance (HD). Deep learning-based approaches improved automated temporal bone segmentation, with strong performance in segmenting larger structures such as the labyrinth, with Dice score over 0.86. However, the segmentation of smaller anatomical structures, such as stapes and chorda tympani, remains a challenge.
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来源期刊
Biomedical engineering advances
Biomedical engineering advances Bioengineering, Biomedical Engineering
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