SlicerCineTrack: An open-source research toolkit for target tracking verification in 3D Slicer.

IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computer methods and programs in biomedicine Pub Date : 2024-11-15 DOI:10.1016/j.cmpb.2024.108497
HaPhan Tran, Teo Mesrkhani, Jacqueline Banh, Nicholas Caro Lopez, Venkat Guru Prasad, Sayeed Jalil, Mubariz Afzal, Elodie Lugez
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Abstract

Purpose: Target motion monitoring plays a significant role in several computer-assisted interventions. However, ensuring the reliability of tracking algorithms can be challenging without adequate tools. We introduce SlicerCineTrack, a free open-source research toolkit, designed to provide users with a user-friendly interface for visualizing their target tracking results.

Methods: SlicerCineTrack was developed as an extension to 3D Slicer. It enables users to visualize target tracking results by sequentially playing back cine medical images, and simultaneously overlaying the target segmentation at the locations indicated by the tracking results. The extension was evaluated by established experts in computer-assisted interventions and image-guided therapy.

Results: SlicerCineTrack is available for download from the 3D Slicer extension catalog for stable releases, and its GitHub repository for preview releases. Evaluation results demonstrate SlicerCineTrack's effectiveness in discriminating between different tracking performances. Moreover, the experts found the extension convenient to use due to its intuitive and user-friendly interface.

Conclusions: SlicerCineTrack was found to be effective at verifying the reliability of tracking algorithms. In turn, SlicerCineTrack shows potential for target tracking verification, as well as algorithm validation and refining through parameter tuning.

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SlicerCineTrack:用于 3D Slicer 中目标跟踪验证的开源研究工具包。
目的:目标运动监测在多种计算机辅助干预中发挥着重要作用。然而,如果没有适当的工具,要确保跟踪算法的可靠性可能具有挑战性。我们介绍的 SlicerCineTrack 是一个免费的开源研究工具包,旨在为用户提供一个友好的界面,使他们的目标跟踪结果可视化:SlicerCineTrack 是作为 3D Slicer 的扩展而开发的。方法:SlicerCineTrack 是作为 3D Slicer 的扩展而开发的,用户可以通过顺序回放电影医学图像,同时在跟踪结果所指示的位置叠加目标分割,从而实现目标跟踪结果的可视化。计算机辅助干预和图像引导治疗领域的知名专家对扩展功能进行了评估:SlicerCineTrack 稳定版可从 3D Slicer 扩展目录下载,预览版可从 GitHub 存储库下载。评估结果表明,SlicerCineTrack 能有效区分不同的跟踪性能。此外,专家们还发现该扩展因其直观和友好的用户界面而易于使用:结论:SlicerCineTrack 可有效验证跟踪算法的可靠性。反过来,SlicerCineTrack 在目标跟踪验证、算法验证以及通过参数调整完善算法方面也显示出了潜力。
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来源期刊
Computer methods and programs in biomedicine
Computer methods and programs in biomedicine 工程技术-工程:生物医学
CiteScore
12.30
自引率
6.60%
发文量
601
审稿时长
135 days
期刊介绍: To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.
期刊最新文献
Dynamic evolution analysis and parameter optimization design of data-driven network infectious disease model. SlicerCineTrack: An open-source research toolkit for target tracking verification in 3D Slicer. Label correlated contrastive learning for medical report generation. BreathVisionNet: A pulmonary-function-guided CNN-transformer hybrid model for expiratory CT image synthesis. Editorial Board
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