股骨、胫骨和腓骨的骨侧刚性配准,用于追踪颞骨变化。

IF 2 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Arttu Ruohola, Ville Haapamäki, Eero Salli, Tuomas Kaseva, Marko Kangasniemi, Sauli Savolainen
{"title":"股骨、胫骨和腓骨的骨侧刚性配准,用于追踪颞骨变化。","authors":"Arttu Ruohola,&nbsp;Ville Haapamäki,&nbsp;Eero Salli,&nbsp;Tuomas Kaseva,&nbsp;Marko Kangasniemi,&nbsp;Sauli Savolainen","doi":"10.1002/acm2.70053","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Multiple myeloma (MM) induces temporal alterations in bone structure, such as osteolytic bone lesions, which are challenging to identify through manual image interpretation. The large variation in radiologists' assessments, even at expert centers, further complicates diagnosis. Automatic image analysis methods, including segmentation and registration, can expedite detecting and tracking these bone changes.</p>\n </section>\n \n <section>\n \n <h3> Purpose</h3>\n \n <p>This study presents an automated pipeline for accurately tracking temporal changes in the femurs, tibiae, and fibulae of MM patients using 3D whole-body CT images. The pipeline leverages image segmentation, rigid registration, and temporal subtraction to accelerate disease monitoring and support clinical decision-making.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>A convolutional neural network (CNN) was trained to segment bones in 3D CT images of 30 MM patients. Nine patients with pre- and post-diagnosis CT scans were used to validate the segmentation and registration process. A two-phase bone-wise rigid registration was applied, followed by temporal subtraction to generate difference images. Segmentation and registration accuracy were assessed using the Dice similarity coefficient (DSC) and mean surface distance (MSD). The proposed method was compared to a non-rigid registration method.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>The neural network segmentation resulted in a mean DSC of 0.93 across all bone types and all test data. The registration accuracy measured by the mean DSC across the test data was at least 0.94 for all bone types. The second phase of rigid registration improved the registration fibulae. Metric-wise, the nonrigid method performed better but diminished lesion visibility in difference images.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>An automated pipeline for the longitudinal tracking of bone alterations was presented. Both segmentation and registration demonstrated high accuracy as measured by DSC and MSD. In the difference images produced by temporal subtraction, osteolytic lesions were clearly visible in the femurs. The methodology lays a solid foundation for future improvements, such as inclusion of the axial spine.</p>\n </section>\n </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 5","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70053","citationCount":"0","resultStr":"{\"title\":\"Bone-wise rigid registration of femur, tibia, and fibula for the tracking of temporal changes\",\"authors\":\"Arttu Ruohola,&nbsp;Ville Haapamäki,&nbsp;Eero Salli,&nbsp;Tuomas Kaseva,&nbsp;Marko Kangasniemi,&nbsp;Sauli Savolainen\",\"doi\":\"10.1002/acm2.70053\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Multiple myeloma (MM) induces temporal alterations in bone structure, such as osteolytic bone lesions, which are challenging to identify through manual image interpretation. The large variation in radiologists' assessments, even at expert centers, further complicates diagnosis. Automatic image analysis methods, including segmentation and registration, can expedite detecting and tracking these bone changes.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Purpose</h3>\\n \\n <p>This study presents an automated pipeline for accurately tracking temporal changes in the femurs, tibiae, and fibulae of MM patients using 3D whole-body CT images. The pipeline leverages image segmentation, rigid registration, and temporal subtraction to accelerate disease monitoring and support clinical decision-making.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>A convolutional neural network (CNN) was trained to segment bones in 3D CT images of 30 MM patients. Nine patients with pre- and post-diagnosis CT scans were used to validate the segmentation and registration process. A two-phase bone-wise rigid registration was applied, followed by temporal subtraction to generate difference images. Segmentation and registration accuracy were assessed using the Dice similarity coefficient (DSC) and mean surface distance (MSD). The proposed method was compared to a non-rigid registration method.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>The neural network segmentation resulted in a mean DSC of 0.93 across all bone types and all test data. The registration accuracy measured by the mean DSC across the test data was at least 0.94 for all bone types. The second phase of rigid registration improved the registration fibulae. Metric-wise, the nonrigid method performed better but diminished lesion visibility in difference images.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>An automated pipeline for the longitudinal tracking of bone alterations was presented. Both segmentation and registration demonstrated high accuracy as measured by DSC and MSD. In the difference images produced by temporal subtraction, osteolytic lesions were clearly visible in the femurs. The methodology lays a solid foundation for future improvements, such as inclusion of the axial spine.</p>\\n </section>\\n </div>\",\"PeriodicalId\":14989,\"journal\":{\"name\":\"Journal of Applied Clinical Medical Physics\",\"volume\":\"26 5\",\"pages\":\"\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2025-03-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70053\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Applied Clinical Medical Physics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/acm2.70053\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Clinical Medical Physics","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/acm2.70053","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

摘要

背景:多发性骨髓瘤(MM)引起骨结构的时间改变,如溶骨性骨病变,这很难通过人工图像解释来识别。放射科医生评估的巨大差异,甚至在专家中心,进一步复杂化了诊断。自动图像分析方法,包括分割和配准,可以加快检测和跟踪这些骨骼变化。目的:本研究提出了一种利用三维全身CT图像准确跟踪MM患者股骨、胫骨和腓骨时间变化的自动化管道。该流水线利用图像分割、刚性配准和时间减法来加速疾病监测和支持临床决策。方法:利用卷积神经网络(CNN)对30例MM患者的三维CT图像进行骨分割。9例患者的诊断前后CT扫描被用来验证分割和注册过程。采用两阶段骨方向刚性配准,然后进行时间相减生成差分图像。使用Dice相似系数(DSC)和平均表面距离(MSD)评估分割和配准精度。将该方法与非刚性配准方法进行了比较。结果:神经网络分割得到所有骨类型和所有测试数据的平均DSC为0.93。通过测试数据的平均DSC测量的配准精度在所有骨类型中至少为0.94。第二阶段刚性配准改善了小腓骨的配准。在度量方面,非刚性方法表现更好,但降低了病变在不同图像中的可见性。结论:提出了一种用于骨改变纵向跟踪的自动化管道。用DSC和MSD测量了分割和配准的精度。在颞骨减影产生的差异图像中,股骨中可以清楚地看到溶解性病变。该方法为未来的改进奠定了坚实的基础,例如包括轴脊柱。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Bone-wise rigid registration of femur, tibia, and fibula for the tracking of temporal changes

Bone-wise rigid registration of femur, tibia, and fibula for the tracking of temporal changes

Background

Multiple myeloma (MM) induces temporal alterations in bone structure, such as osteolytic bone lesions, which are challenging to identify through manual image interpretation. The large variation in radiologists' assessments, even at expert centers, further complicates diagnosis. Automatic image analysis methods, including segmentation and registration, can expedite detecting and tracking these bone changes.

Purpose

This study presents an automated pipeline for accurately tracking temporal changes in the femurs, tibiae, and fibulae of MM patients using 3D whole-body CT images. The pipeline leverages image segmentation, rigid registration, and temporal subtraction to accelerate disease monitoring and support clinical decision-making.

Methods

A convolutional neural network (CNN) was trained to segment bones in 3D CT images of 30 MM patients. Nine patients with pre- and post-diagnosis CT scans were used to validate the segmentation and registration process. A two-phase bone-wise rigid registration was applied, followed by temporal subtraction to generate difference images. Segmentation and registration accuracy were assessed using the Dice similarity coefficient (DSC) and mean surface distance (MSD). The proposed method was compared to a non-rigid registration method.

Results

The neural network segmentation resulted in a mean DSC of 0.93 across all bone types and all test data. The registration accuracy measured by the mean DSC across the test data was at least 0.94 for all bone types. The second phase of rigid registration improved the registration fibulae. Metric-wise, the nonrigid method performed better but diminished lesion visibility in difference images.

Conclusions

An automated pipeline for the longitudinal tracking of bone alterations was presented. Both segmentation and registration demonstrated high accuracy as measured by DSC and MSD. In the difference images produced by temporal subtraction, osteolytic lesions were clearly visible in the femurs. The methodology lays a solid foundation for future improvements, such as inclusion of the axial spine.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
3.60
自引率
19.00%
发文量
331
审稿时长
3 months
期刊介绍: Journal of Applied Clinical Medical Physics is an international Open Access publication dedicated to clinical medical physics. JACMP welcomes original contributions dealing with all aspects of medical physics from scientists working in the clinical medical physics around the world. JACMP accepts only online submission. JACMP will publish: -Original Contributions: Peer-reviewed, investigations that represent new and significant contributions to the field. Recommended word count: up to 7500. -Review Articles: Reviews of major areas or sub-areas in the field of clinical medical physics. These articles may be of any length and are peer reviewed. -Technical Notes: These should be no longer than 3000 words, including key references. -Letters to the Editor: Comments on papers published in JACMP or on any other matters of interest to clinical medical physics. These should not be more than 1250 (including the literature) and their publication is only based on the decision of the editor, who occasionally asks experts on the merit of the contents. -Book Reviews: The editorial office solicits Book Reviews. -Announcements of Forthcoming Meetings: The Editor may provide notice of forthcoming meetings, course offerings, and other events relevant to clinical medical physics. -Parallel Opposed Editorial: We welcome topics relevant to clinical practice and medical physics profession. The contents can be controversial debate or opposed aspects of an issue. One author argues for the position and the other against. Each side of the debate contains an opening statement up to 800 words, followed by a rebuttal up to 500 words. Readers interested in participating in this series should contact the moderator with a proposed title and a short description of the topic
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信