Segmentation of Coronary Artery Stenosis in X-ray Angiography using Mamba Models
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909585383620608 |
|---|---|
| author | Rostami, Ali Fouladi, Fatemeh Sajedi, Hedieh |
| author_facet | Rostami, Ali Fouladi, Fatemeh Sajedi, Hedieh |
| contents | Coronary artery disease stands as one of the primary contributors to global mortality rates. The automated identification of coronary artery stenosis from X-ray images plays a critical role in the diagnostic process for coronary heart disease. This task is challenging due to the complex structure of coronary arteries, intrinsic noise in X-ray images, and the fact that stenotic coronary arteries appear narrow and blurred in X-ray angiographies. This study employs five different variants of the Mamba-based model and one variant of the Swin Transformer-based model, primarily based on the U-Net architecture, for the localization of stenosis in Coronary artery disease. Our best results showed an F1 score of 68.79% for the U-Mamba BOT model, representing an 11.8% improvement over the semi-supervised approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02568 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Segmentation of Coronary Artery Stenosis in X-ray Angiography using Mamba Models Rostami, Ali Fouladi, Fatemeh Sajedi, Hedieh Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Coronary artery disease stands as one of the primary contributors to global mortality rates. The automated identification of coronary artery stenosis from X-ray images plays a critical role in the diagnostic process for coronary heart disease. This task is challenging due to the complex structure of coronary arteries, intrinsic noise in X-ray images, and the fact that stenotic coronary arteries appear narrow and blurred in X-ray angiographies. This study employs five different variants of the Mamba-based model and one variant of the Swin Transformer-based model, primarily based on the U-Net architecture, for the localization of stenosis in Coronary artery disease. Our best results showed an F1 score of 68.79% for the U-Mamba BOT model, representing an 11.8% improvement over the semi-supervised approach. |
| title | Segmentation of Coronary Artery Stenosis in X-ray Angiography using Mamba Models |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.02568 |