Panoptic Segmentation and Labelling of Lumbar Spine Vertebrae using Modified Attention Unet
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910426565967872 |
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| author | Pal, Rikathi Saha, Priya Ghoshal, Somoballi Chakrabarti, Amlan Sur-Kolay, Susmita |
| author_facet | Pal, Rikathi Saha, Priya Ghoshal, Somoballi Chakrabarti, Amlan Sur-Kolay, Susmita |
| contents | Segmentation and labeling of vertebrae in MRI images of the spine are critical for the diagnosis of illnesses and abnormalities. These steps are indispensable as MRI technology provides detailed information about the tissue structure of the spine. Both supervised and unsupervised segmentation methods exist, yet acquiring sufficient data remains challenging for achieving high accuracy. In this study, we propose an enhancing approach based on modified attention U-Net architecture for panoptic segmentation of 3D sliced MRI data of the lumbar spine. Our method achieves an impressive accuracy of 99.5\% by incorporating novel masking logic, thus significantly advancing the state-of-the-art in vertebral segmentation and labeling. This contributes to more precise and reliable diagnosis and treatment planning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_18291 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Panoptic Segmentation and Labelling of Lumbar Spine Vertebrae using Modified Attention Unet Pal, Rikathi Saha, Priya Ghoshal, Somoballi Chakrabarti, Amlan Sur-Kolay, Susmita Computer Vision and Pattern Recognition Artificial Intelligence Segmentation and labeling of vertebrae in MRI images of the spine are critical for the diagnosis of illnesses and abnormalities. These steps are indispensable as MRI technology provides detailed information about the tissue structure of the spine. Both supervised and unsupervised segmentation methods exist, yet acquiring sufficient data remains challenging for achieving high accuracy. In this study, we propose an enhancing approach based on modified attention U-Net architecture for panoptic segmentation of 3D sliced MRI data of the lumbar spine. Our method achieves an impressive accuracy of 99.5\% by incorporating novel masking logic, thus significantly advancing the state-of-the-art in vertebral segmentation and labeling. This contributes to more precise and reliable diagnosis and treatment planning. |
| title | Panoptic Segmentation and Labelling of Lumbar Spine Vertebrae using Modified Attention Unet |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2404.18291 |