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Updated: Sep 7, 2025
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
Multi-constraints based deep learning model for automated segmentation and diagnosis of coronary artery disease in
Mona Algarni1,2, Abdulkader Al-Rezqi3, Faisal Saeed1,4
1College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia.
Insights
This study introduces an Automated Segmentation and Diagnosis of Coronary Artery Disease (ASCARIS) model to improve coronary artery disease detection from X-ray images. The ASCARIS model demonstrates superior performance in classifying coronary artery disease, enhancing diagnostic accuracy.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Medicine
- Cardiovascular Disease Diagnosis
Background:
- Coronary artery disease (CAD) detection from X-ray angiography is challenging due to image noise, low contrast, respiratory motion, and varying vessel angles.
- Existing methods struggle with artifacts and accurately segmenting complex vascular structures.
Purpose of the Study:
- To propose an Automated Segmentation and Diagnosis of Coronary Artery Disease (ASCARIS) model to overcome challenges in CAD detection from X-ray images.
- To enhance the accuracy and reliability of CAD diagnosis through advanced image processing and deep learning techniques.
Main Methods:
- Preprocessing using a modified Wiener filter for noise removal and optimized maximum principal curvature for contrast enhancement.
- Segmentation employing an Attention-based Nested U-Net with an attention estimator to handle overlapping arteries, further refined by angle estimation.
- Classification using a VGG-16 architecture for feature extraction from segmented images.
Main Results:
- The ASCARIS model achieved superior performance across multiple evaluation metrics including accuracy, sensitivity, specificity, and segmentation accuracy compared to existing methods.
- The model effectively removed background artifacts and improved the visualization of vascular structures.
- Optimized classification of coronary artery disease was achieved.
Conclusions:
- The proposed ASCARIS model represents a significant advancement in automated CAD detection from X-ray images.
- The model's ability to handle image imperfections and complex anatomy leads to more accurate and reliable diagnoses.
- ASCARIS offers a promising tool for clinical application in cardiovascular disease screening.
Background:
The detection of coronary artery disease (CAD) from the X-ray coronary angiography is a crucial process which is hindered by various issues such as presence of noise, insufficient contrast of the input images along with the uncertainties caused by the motion due to respiration and variation of angles of vessels.
Methods:
In this article, an Automated Segmentation and Diagnosis of Coronary Artery Disease (ASCARIS) model is proposed in order to overcome the prevailing challenges in detection of CAD from the X-ray images. Initially, the preprocessing of the input images was carried out by using the modified wiener filter for the removal of both internal and external noise pixels from the images. Then, the enhancement of contrast was carried out by utilizing the optimized maximum principal curvature to preserve the edge information thereby contributing to increasing the segmentation accuracy. Further, the binarization of enhanced images was executed by the means of OTSU thresholding. The segmentation of coronary arteries was performed by implementing the Attention-based Nested U-Net, in which the attention estimator was incorporated to overcome the difficulties caused by intersections and overlapped arteries. The increased segmentation accuracy was achieved by performing angle estimation. Finally, the VGG-16 based architecture was implemented to extract threefold features from the segmented image to perform classification of X-ray images into normal and abnormal classes.
Results:
The experimentation of the proposed ASCARIS model was carried out in the MATLAB R2020a simulation tool and the evaluation of the proposed model was compared with several existing approaches in terms of accuracy, sensitivity, specificity, revised contrast to noise ratio, mean square error, dice coefficient, Jaccard similarity, Hausdorff distance, Peak signal-to-noise ratio (PSNR), segmentation accuracy and ROC curve.
Discussion:
The results obtained conclude that the proposed model outperforms the existing approaches in all the evaluation metrics thereby achieving optimized classification of CAD. The proposed method removes the large number of background artifacts and obtains a better vascular structure.
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