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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/2402.19263 |
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| _version_ | 1866913248418201600 |
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| author | Kundu, Soumya Snigdha Mo, Yuanhan Srikijkasemwat, Nicharee Papiez, Bartłomiej W. |
| author_facet | Kundu, Soumya Snigdha Mo, Yuanhan Srikijkasemwat, Nicharee Papiez, Bartłomiej W. |
| contents | The development and progression of arthritis is strongly associated with osteophytes, which are small and elusive bone growths. This paper presents one of the first efforts towards automated spinal osteophyte detection in spinal X-rays. A novel automated patch extraction process, called SegPatch, has been proposed based on deep learning-driven vertebrae segmentation and the enlargement of mask contours. A final patch classification accuracy of 84.5\% is secured, surpassing a baseline tiling-based patch generation technique by 9.5%. This demonstrates that even with limited annotations, SegPatch can deliver superior performance for detection of tiny structures such as osteophytes. The proposed approach has potential to assist clinicians in expediting the process of manually identifying osteophytes in spinal X-ray. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_19263 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Spinal Osteophyte Detection via Robust Patch Extraction on minimally annotated X-rays Kundu, Soumya Snigdha Mo, Yuanhan Srikijkasemwat, Nicharee Papiez, Bartłomiej W. Computer Vision and Pattern Recognition Artificial Intelligence The development and progression of arthritis is strongly associated with osteophytes, which are small and elusive bone growths. This paper presents one of the first efforts towards automated spinal osteophyte detection in spinal X-rays. A novel automated patch extraction process, called SegPatch, has been proposed based on deep learning-driven vertebrae segmentation and the enlargement of mask contours. A final patch classification accuracy of 84.5\% is secured, surpassing a baseline tiling-based patch generation technique by 9.5%. This demonstrates that even with limited annotations, SegPatch can deliver superior performance for detection of tiny structures such as osteophytes. The proposed approach has potential to assist clinicians in expediting the process of manually identifying osteophytes in spinal X-ray. |
| title | Spinal Osteophyte Detection via Robust Patch Extraction on minimally annotated X-rays |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2402.19263 |