Registration of Longitudinal Spine CTs for Monitoring Lesion Growth
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arXiv
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| Format: | Preprint |
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2024
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| author | Sanhinova, Malika Haouchine, Nazim Pieper, Steve D. Wells III, William M. Balboni, Tracy A. Spektor, Alexander Huynh, Mai Anh Guenette, Jeffrey P. Czajkowski, Bryan Caplan, Sarah Doyle, Patrick Kang, Heejoo Hackney, David B. Alkalay, Ron N. |
| author_facet | Sanhinova, Malika Haouchine, Nazim Pieper, Steve D. Wells III, William M. Balboni, Tracy A. Spektor, Alexander Huynh, Mai Anh Guenette, Jeffrey P. Czajkowski, Bryan Caplan, Sarah Doyle, Patrick Kang, Heejoo Hackney, David B. Alkalay, Ron N. |
| contents | Accurate and reliable registration of longitudinal spine images is essential for assessment of disease progression and surgical outcome. Implementing a fully automatic and robust registration is crucial for clinical use, however, it is challenging due to substantial change in shape and appearance due to lesions. In this paper we present a novel method to automatically align longitudinal spine CTs and accurately assess lesion progression. Our method follows a two-step pipeline where vertebrae are first automatically localized, labeled and 3D surfaces are generated using a deep learning model, then longitudinally aligned using a Gaussian mixture model surface registration. We tested our approach on 37 vertebrae, from 5 patients, with baseline CTs and 3, 6, and 12 months follow-ups leading to 111 registrations. Our experiment showed accurate registration with an average Hausdorff distance of 0.65 mm and average Dice score of 0.92. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09341 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Registration of Longitudinal Spine CTs for Monitoring Lesion Growth Sanhinova, Malika Haouchine, Nazim Pieper, Steve D. Wells III, William M. Balboni, Tracy A. Spektor, Alexander Huynh, Mai Anh Guenette, Jeffrey P. Czajkowski, Bryan Caplan, Sarah Doyle, Patrick Kang, Heejoo Hackney, David B. Alkalay, Ron N. Image and Video Processing Computer Vision and Pattern Recognition Accurate and reliable registration of longitudinal spine images is essential for assessment of disease progression and surgical outcome. Implementing a fully automatic and robust registration is crucial for clinical use, however, it is challenging due to substantial change in shape and appearance due to lesions. In this paper we present a novel method to automatically align longitudinal spine CTs and accurately assess lesion progression. Our method follows a two-step pipeline where vertebrae are first automatically localized, labeled and 3D surfaces are generated using a deep learning model, then longitudinally aligned using a Gaussian mixture model surface registration. We tested our approach on 37 vertebrae, from 5 patients, with baseline CTs and 3, 6, and 12 months follow-ups leading to 111 registrations. Our experiment showed accurate registration with an average Hausdorff distance of 0.65 mm and average Dice score of 0.92. |
| title | Registration of Longitudinal Spine CTs for Monitoring Lesion Growth |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.09341 |