用于医学影像中膝关节自动分割的 CNN 架构比较分析:性能评估

IF 2.6 3区 工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Anna Ghidotti, A. Vitali, D. Regazzoni, Miri Weiss Cohen, C. Rizzi
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引用次数: 0

摘要

解剖组件的分割是创建精确逼真的人体 3D 模型的重要步骤,这些模型被用于包括骨科在内的许多临床应用中。最近,许多深度学习方法被提出来解决人工分割的问题,而人工分割既耗时又依赖于操作者。在本研究中,SegResNet 从其他领域(如脑肿瘤)改编而来,用于从磁共振图像中分割膝盖骨。在评估指标(如骰子相似系数和豪斯多夫距离)方面,该算法与著名的 U-Net 进行了比较。在训练阶段,测试了各种超参数组合,如历时和学习率,以确定哪种组合能产生最准确的结果。根据它们的可比结果,U-Net 和 SegResNet 在准确分割股骨方面都表现出色。骰子相似系数为 0.94,豪斯多夫距离小于或等于 1 毫米,这表明两个模型都能有效捕捉股骨的解剖边界。根据这项研究的结果,SegResNet 是自动创建三维股骨模型的可行选择。未来,SegResNet 在实际环境中的性能和适用性将通过各种数据集和临床场景得到进一步验证和测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative Analysis of CNN Architectures for Automated Knee Segmentation in Medical Imaging: a Performance Evaluation
Segmentation of anatomical components is a major step in creating accurate and realistic 3D models of the human body, which are used in many clinical applications, including orthopedics. Recently, many deep learning approaches have been proposed to solve the problem of manual segmentation, that is time-consuming and operator-dependent. In the present study, SegResNet has been adapted from other domains, such as brain tumor, to segment knee bones from Magnetic Resonance images. This algorithm has been compared to the well-known U-Net in terms of evaluation metrics, such as Dice Similarity Coefficient and Hausdorff Distance. In the training phase, various combinations of hyper-parameters, such as epochs and learning rates, have been tested to determine which combination produced the most accurate results. Based on their comparable results, both U-Net and SegResNet performed well in accurately segmenting the femur. Dice Similarity Coefficients of 0.94 and Hausdorff Distances less than or equal to 1 mm indicate that both models are effective at capturing anatomical boundaries in the femur. According to the results of this study, SegResNet is a viable option for automating the creation of 3D femur models. In the future, the performance and applicability of SegResNet in real-world settings will be further validated and tested using a variety of datasets and clinical scenarios.
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来源期刊
CiteScore
6.30
自引率
12.90%
发文量
100
审稿时长
6 months
期刊介绍: The ASME Journal of Computing and Information Science in Engineering (JCISE) publishes articles related to Algorithms, Computational Methods, Computing Infrastructure, Computer-Interpretable Representations, Human-Computer Interfaces, Information Science, and/or System Architectures that aim to improve some aspect of product and system lifecycle (e.g., design, manufacturing, operation, maintenance, disposal, recycling etc.). Applications considered in JCISE manuscripts should be relevant to the mechanical engineering discipline. Papers can be focused on fundamental research leading to new methods, or adaptation of existing methods for new applications. Scope: Advanced Computing Infrastructure; Artificial Intelligence; Big Data and Analytics; Collaborative Design; Computer Aided Design; Computer Aided Engineering; Computer Aided Manufacturing; Computational Foundations for Additive Manufacturing; Computational Foundations for Engineering Optimization; Computational Geometry; Computational Metrology; Computational Synthesis; Conceptual Design; Cybermanufacturing; Cyber Physical Security for Factories; Cyber Physical System Design and Operation; Data-Driven Engineering Applications; Engineering Informatics; Geometric Reasoning; GPU Computing for Design and Manufacturing; Human Computer Interfaces/Interactions; Industrial Internet of Things; Knowledge Engineering; Information Management; Inverse Methods for Engineering Applications; Machine Learning for Engineering Applications; Manufacturing Planning; Manufacturing Automation; Model-based Systems Engineering; Multiphysics Modeling and Simulation; Multiscale Modeling and Simulation; Multidisciplinary Optimization; Physics-Based Simulations; Process Modeling for Engineering Applications; Qualification, Verification and Validation of Computational Models; Symbolic Computing for Engineering Applications; Tolerance Modeling; Topology and Shape Optimization; Virtual and Augmented Reality Environments; Virtual Prototyping
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