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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.07132 |
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| _version_ | 1866909439849660416 |
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| author | Huang, Wei-Lun Xue, Minghao Liu, Zhiyou Tashayyod, Davood Kang, Jun Gandjbakhche, Amir Kazhdan, Misha Armand, Mehran |
| author_facet | Huang, Wei-Lun Xue, Minghao Liu, Zhiyou Tashayyod, Davood Kang, Jun Gandjbakhche, Amir Kazhdan, Misha Armand, Mehran |
| contents | Melanoma is the most deadly form of skin cancer. Tracking the evolution of nevi and detecting new lesions across the body is essential for the early detection of melanoma. Despite prior work on longitudinal tracking of skin lesions in 3D total body photography, there are still several challenges, including 1) low accuracy for finding correct lesion pairs across scans, 2) sensitivity to noisy lesion detection, and 3) lack of large-scale datasets with numerous annotated lesion pairs. We propose a framework that takes in a pair of 3D textured meshes, matches lesions in the context of total body photography, and identifies unmatchable lesions. We start by computing correspondence maps bringing the source and target meshes to a template mesh. Using these maps to define source/target signals over the template domain, we construct a flow field aligning the mapped signals. The initial correspondence maps are then refined by advecting forward/backward along the vector field. Finally, lesion assignment is performed using the refined correspondence maps. We propose the first large-scale dataset for skin lesion tracking with 25K lesion pairs across 198 subjects. The proposed method achieves a success rate of 89.9% (at 10 mm criterion) for all pairs of annotated lesions and a matching accuracy of 98.2% for subjects with more than 200 lesions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_07132 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Revisiting Lesion Tracking in 3D Total Body Photography Huang, Wei-Lun Xue, Minghao Liu, Zhiyou Tashayyod, Davood Kang, Jun Gandjbakhche, Amir Kazhdan, Misha Armand, Mehran Computer Vision and Pattern Recognition Melanoma is the most deadly form of skin cancer. Tracking the evolution of nevi and detecting new lesions across the body is essential for the early detection of melanoma. Despite prior work on longitudinal tracking of skin lesions in 3D total body photography, there are still several challenges, including 1) low accuracy for finding correct lesion pairs across scans, 2) sensitivity to noisy lesion detection, and 3) lack of large-scale datasets with numerous annotated lesion pairs. We propose a framework that takes in a pair of 3D textured meshes, matches lesions in the context of total body photography, and identifies unmatchable lesions. We start by computing correspondence maps bringing the source and target meshes to a template mesh. Using these maps to define source/target signals over the template domain, we construct a flow field aligning the mapped signals. The initial correspondence maps are then refined by advecting forward/backward along the vector field. Finally, lesion assignment is performed using the refined correspondence maps. We propose the first large-scale dataset for skin lesion tracking with 25K lesion pairs across 198 subjects. The proposed method achieves a success rate of 89.9% (at 10 mm criterion) for all pairs of annotated lesions and a matching accuracy of 98.2% for subjects with more than 200 lesions. |
| title | Revisiting Lesion Tracking in 3D Total Body Photography |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.07132 |