Open Access

ARTICLE

Hybrid Multi-Scale 3D Segmentation Framework for Automated Stenosis Detection

Angelin Gladston¹, Swedha Velraj^2,*, Harini Nadarajan¹

1 Department of CSE, College of Engineering, Anna University, Chennai, 600025, India
2 Department of Information Technology, Saveetha Engineering College, Chennai, 602105, India

* Corresponding Author: Swedha Velraj. Email:

Congenital Heart Disease 2025, 20(6), 769-792. https://doi.org/10.32604/chd.2025.068879

Received 09 June 2025; Accepted 04 November 2025; Issue published 10 February 2026

Abstract

Background: Coronary artery disease (CAD) is a major global health concern requiring efficient and accurate diagnostic methods. Manual interpretation of coronary computed tomography angiography (CTA) images is time-consuming and prone to interobserver variability, underscoring the need for automated segmentation and stenosis detection tools. Methods: This study presents a hybrid multi-scale 3D segmentation framework utilizing both 3D U-Net and Enhanced 3D U-Net architectures, designed to balance computational efficiency and anatomical precision. Processed CTA images from the ImageCAS dataset underwent data standardization, normalization, and augmentation. The framework applies ensemble learning to merge coarse and fine segmentation masks, followed by advanced post-processing techniques, including connected component analysis and centerline extraction, to refine vessel delineation. Stenosis regions are detected using the Enhanced 3D U-Net and morphological operations for accurate localization. Results: The proposed pipeline achieved near-perfect segmentation accuracy (0.9993) and a Dice similarity coefficient of 0.8539 for coronary artery delineation. Precision, recall, and F1 scores for stenosis detection were 0.8418, 0.8289, and 0.8397, respectively. The dual-model approach demonstrated robust performance across varied anatomical structures and effectively localized stenotic regions, indicating clear superiority over conventional models. Conclusion: This hybrid framework enables highly reliable and automated coronary artery segmentation and stenosis detection from 3D CTA images. By reducing reliance on manual interpretation and enhancing diagnostic consistency, the proposed method holds strong potential to improve clinical workflows for CAD diagnosis and management.

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Keywords

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