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, Ville Haapamäki, Eero Salli, Tuomas Kaseva, Marko Kangasniemi, 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, Ville Haapamäki, Eero Salli, Tuomas Kaseva, Marko Kangasniemi, 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. 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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.
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.
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