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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/2404.18731 |
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| _version_ | 1866916559446867968 |
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| author | Yerebakan, Halid Ziya Shinagawa, Yoshihisa Valadez, Gerardo Hermosillo |
| author_facet | Yerebakan, Halid Ziya Shinagawa, Yoshihisa Valadez, Gerardo Hermosillo |
| contents | Organ segmentation is a fundamental task in medical imaging since it is useful for many clinical automation pipelines. However, some tasks do not require full segmentation. Instead, a classifier can identify the selected organ without segmenting the entire volume. In this study, we demonstrate a classifier based method to obtain organ labels in real time by using a large context size with a sparse data sampling strategy. Although our method operates as an independent classifier at query locations, it can generate full segmentations by querying grid locations at any resolution, offering faster performance than segmentation algorithms. We compared our method with existing segmentation techniques, demonstrating its superior runtime potential for practical applications in medical imaging. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_18731 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Real Time Multi Organ Classification on Computed Tomography Images Yerebakan, Halid Ziya Shinagawa, Yoshihisa Valadez, Gerardo Hermosillo Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Organ segmentation is a fundamental task in medical imaging since it is useful for many clinical automation pipelines. However, some tasks do not require full segmentation. Instead, a classifier can identify the selected organ without segmenting the entire volume. In this study, we demonstrate a classifier based method to obtain organ labels in real time by using a large context size with a sparse data sampling strategy. Although our method operates as an independent classifier at query locations, it can generate full segmentations by querying grid locations at any resolution, offering faster performance than segmentation algorithms. We compared our method with existing segmentation techniques, demonstrating its superior runtime potential for practical applications in medical imaging. |
| title | Real Time Multi Organ Classification on Computed Tomography Images |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2404.18731 |