A novel framework for differentiating vessel-like objects in coronarography images
Insights
This study introduces a novel algorithm to refine coronary artery segmentation, removing artifacts like catheters. This 99% accurate method enhances AI-driven diagnosis for Coronary Artery Disease, improving patient care.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Cardiovascular Disease
Background:
- Coronary Artery Disease (CAD) is a leading global cause of mortality.
- Limited access to specialized care necessitates automated diagnostic tools.
- Current coronary angiography segmentation methods struggle with artifacts, hindering automated diagnosis.
Purpose of the Study:
- To develop a post-segmentation refinement algorithm for precise coronary artery segmentation.
- To eliminate vessel-like artifacts from segmentation results.
- To enable automated AI-supported diagnosis workflows for CAD.
Main Methods:
- A two-step refinement algorithm using XGBoost Classifier and a neighborhood filter.
- Extraction of image features from binary coronary artery segmentation.
- Utilizing Tamura features for artifact differentiation.
Main Results:
- The algorithm achieves 99% accuracy in differentiating coronary arteries from artifacts.
- Successfully removes artifacts like catheters and stitches from segmentation.
- Enables automated quantitative evaluation of lesions in coronary angiography.
Conclusions:
- The proposed algorithm significantly improves coronary artery segmentation accuracy.
- Facilitates the automation of AI-supported diagnosis for CAD.
- Potential for application in other medical imaging domains for artifact removal.
Abstract:
Coronary Artery Disease is the leading cause of death worldwide. Its prevalence will grow while access to specialized medical care will be further limited due to staff shortages. Therefore, any facilitation of diagnosis or treatment is of paramount importance. The diagnosis based on Coronary Angiography can be automated to perform a quantitative evaluation of lesions. This requires precise segmentation of coronary arteries. At the moment, the state-of-the-art algorithms fail to eliminate vessel-like artifacts that are wrongly included in segmentation results (e.g. catheters, stitches). This is a bottleneck for the automatization of the diagnosis workflow that precedes clinical action. In this paper, we propose a 2-step post-segmentation refinement algorithm. A binary segmentation of the coronary arteries is used to extract image features - inputs for an XGBoost Classifier. Its predictions are improved by a neighborhood filter that leverages contextual information to assign correct labels. The algorithm is primarily concerned with differentiating vessels from other vessel-like objects and does so with a 99% accuracy rate. It takes advantage of an original local description of Tamura features, which proved to be one of the most influential factors in decision-making. As a result, the segmentation of coronary arteries is cleaned from artifacts, enabling AI-supported diagnosis workflows to be automated. After re-training, the proposed method can be used to eliminate post-segmentation artifacts in other medical domains.Clinical relevance- The algorithm proposed in this paper allows for the development of software that could automatically calculate the Syntax Score in real time. This would shorten diagnostics time and allow for immediate action in critical cases.
