AutoComb: Automated Comb Sign Detector for 3D CTE Scans
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866910851012755456 |
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| author | Gupta, Shashwat Gupta, Sarthak Agrawal, Akshan Naaz, Mahim Yadav, Rajanikanth Bagade, Priyanka |
| author_facet | Gupta, Shashwat Gupta, Sarthak Agrawal, Akshan Naaz, Mahim Yadav, Rajanikanth Bagade, Priyanka |
| contents | Comb Sign is an important imaging biomarker to detect multiple gastrointestinal diseases. It shows up as increased blood flow along the intestinal wall indicating potential abnormality, which helps doctors diagnose inflammatory conditions. Despite its clinical significance, current detection methods are manual, time-intensive, and prone to subjective interpretation due to the need for multi-planar image-orientation. To the best of our knowledge, we are the first to propose a fully automated technique for the detection of Comb Sign from CTE scans. Our novel approach is based on developing a probabilistic map that shows areas of pathological hypervascularity by identifying fine vascular bifurcations and wall enhancement via processing through stepwise algorithmic modules. These modules include utilising deep learning segmentation model, a Gaussian Mixture Model (GMM), vessel extraction using vesselness filter, iterative probabilistic enhancement of vesselness via neighborhood maximization and a distance-based weighting scheme over the vessels. Experimental results demonstrate that our pipeline effectively identifies Comb Sign, offering an objective, accurate, and reliable tool to enhance diagnostic accuracy in Crohn's disease and related hypervascular conditions where Comb Sign is considered as one of the important biomarkers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_21311 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AutoComb: Automated Comb Sign Detector for 3D CTE Scans Gupta, Shashwat Gupta, Sarthak Agrawal, Akshan Naaz, Mahim Yadav, Rajanikanth Bagade, Priyanka Image and Video Processing Computer Vision and Pattern Recognition Comb Sign is an important imaging biomarker to detect multiple gastrointestinal diseases. It shows up as increased blood flow along the intestinal wall indicating potential abnormality, which helps doctors diagnose inflammatory conditions. Despite its clinical significance, current detection methods are manual, time-intensive, and prone to subjective interpretation due to the need for multi-planar image-orientation. To the best of our knowledge, we are the first to propose a fully automated technique for the detection of Comb Sign from CTE scans. Our novel approach is based on developing a probabilistic map that shows areas of pathological hypervascularity by identifying fine vascular bifurcations and wall enhancement via processing through stepwise algorithmic modules. These modules include utilising deep learning segmentation model, a Gaussian Mixture Model (GMM), vessel extraction using vesselness filter, iterative probabilistic enhancement of vesselness via neighborhood maximization and a distance-based weighting scheme over the vessels. Experimental results demonstrate that our pipeline effectively identifies Comb Sign, offering an objective, accurate, and reliable tool to enhance diagnostic accuracy in Crohn's disease and related hypervascular conditions where Comb Sign is considered as one of the important biomarkers. |
| title | AutoComb: Automated Comb Sign Detector for 3D CTE Scans |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.21311 |