Accuracy of an articulated head-and-neck motion model using deep learning-based instance segmentation of skeletal bones in CT scans for image registration in radiotherapy
Knowing about anatomical deformations in patient images is crucial for adaptive image-guided radiation therapy. Biomechanical models ensure biofidelity in deformable image registration, but manual contouring limits their clinical use. We investigate the application of automatically generated contour...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21681163.2025.2455752 |
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| author | Alexandra Walter Cornelius J. Bauer Ama Katseena Yawson Philipp Hoegen-Saßmannshausen Sebastian Adeberg Jürgen Debus Oliver Jäkel Martin Frank Kristina Giske |
| author_facet | Alexandra Walter Cornelius J. Bauer Ama Katseena Yawson Philipp Hoegen-Saßmannshausen Sebastian Adeberg Jürgen Debus Oliver Jäkel Martin Frank Kristina Giske |
| author_sort | Alexandra Walter |
| collection | DOAJ |
| description | Knowing about anatomical deformations in patient images is crucial for adaptive image-guided radiation therapy. Biomechanical models ensure biofidelity in deformable image registration, but manual contouring limits their clinical use. We investigate the application of automatically generated contours for a biomechanical registration model in head and neck cancer treatment. For that, we automatically generate individual bone segmentations on planning CT scans examining a custom-trained nnU-Net model and the ready-trained TotalSegmentator model. Both sets of segmentations are evaluated using DICE, Hausdorff Distance and surface DICE. We investigate their impact on the build-up of the biomechanical articulated skeleton model by deviations in joint positioning and CT-CT registration accuracy using target registration error (TRE). The custom-trained model achieves 1.51 ± 0.26 mm TRE, with no significant difference in registration accuracy. While the TotalSegmentator does not provide all structures needed for the complete biomechanical model build-up. Overall, deep learning–based automatic bone segmentation can replace manual contouring in this model, matching its performance. |
| format | Article |
| id | doaj-art-9a44d996b80646578cba0afb9b5d363f |
| institution | Kabale University |
| issn | 2168-1163 2168-1171 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
| spelling | doaj-art-9a44d996b80646578cba0afb9b5d363f2025-01-27T00:23:21ZengTaylor & Francis GroupComputer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization2168-11632168-11712025-12-0113110.1080/21681163.2025.2455752Accuracy of an articulated head-and-neck motion model using deep learning-based instance segmentation of skeletal bones in CT scans for image registration in radiotherapyAlexandra Walter0Cornelius J. Bauer1Ama Katseena Yawson2Philipp Hoegen-Saßmannshausen3Sebastian Adeberg4Jürgen Debus5Oliver Jäkel6Martin Frank7Kristina Giske8Division of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, GermanyDivision of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, GermanyDivision of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, GermanyDivision of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, GermanyDepartment of Radiation Oncology, UKGM Marburg, Marburg, GermanyDepartment of Radiation Oncology, Heidelberg University Hospital, Heidelberg, GermanyDivision of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, GermanyScientific Computing Center, Karlsruhe Institute of Technology (KIT), Karlsruhe, GermanyDivision of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, GermanyKnowing about anatomical deformations in patient images is crucial for adaptive image-guided radiation therapy. Biomechanical models ensure biofidelity in deformable image registration, but manual contouring limits their clinical use. We investigate the application of automatically generated contours for a biomechanical registration model in head and neck cancer treatment. For that, we automatically generate individual bone segmentations on planning CT scans examining a custom-trained nnU-Net model and the ready-trained TotalSegmentator model. Both sets of segmentations are evaluated using DICE, Hausdorff Distance and surface DICE. We investigate their impact on the build-up of the biomechanical articulated skeleton model by deviations in joint positioning and CT-CT registration accuracy using target registration error (TRE). The custom-trained model achieves 1.51 ± 0.26 mm TRE, with no significant difference in registration accuracy. While the TotalSegmentator does not provide all structures needed for the complete biomechanical model build-up. Overall, deep learning–based automatic bone segmentation can replace manual contouring in this model, matching its performance.https://www.tandfonline.com/doi/10.1080/21681163.2025.2455752Biomechanical modellingbiofidelityimage registrationhead and neck cancermedical image segmentation |
| spellingShingle | Alexandra Walter Cornelius J. Bauer Ama Katseena Yawson Philipp Hoegen-Saßmannshausen Sebastian Adeberg Jürgen Debus Oliver Jäkel Martin Frank Kristina Giske Accuracy of an articulated head-and-neck motion model using deep learning-based instance segmentation of skeletal bones in CT scans for image registration in radiotherapy Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization Biomechanical modelling biofidelity image registration head and neck cancer medical image segmentation |
| title | Accuracy of an articulated head-and-neck motion model using deep learning-based instance segmentation of skeletal bones in CT scans for image registration in radiotherapy |
| title_full | Accuracy of an articulated head-and-neck motion model using deep learning-based instance segmentation of skeletal bones in CT scans for image registration in radiotherapy |
| title_fullStr | Accuracy of an articulated head-and-neck motion model using deep learning-based instance segmentation of skeletal bones in CT scans for image registration in radiotherapy |
| title_full_unstemmed | Accuracy of an articulated head-and-neck motion model using deep learning-based instance segmentation of skeletal bones in CT scans for image registration in radiotherapy |
| title_short | Accuracy of an articulated head-and-neck motion model using deep learning-based instance segmentation of skeletal bones in CT scans for image registration in radiotherapy |
| title_sort | accuracy of an articulated head and neck motion model using deep learning based instance segmentation of skeletal bones in ct scans for image registration in radiotherapy |
| topic | Biomechanical modelling biofidelity image registration head and neck cancer medical image segmentation |
| url | https://www.tandfonline.com/doi/10.1080/21681163.2025.2455752 |
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