CASR-Net: An Image Processing-focused Deep Learning-based Coronary Artery Segmentation and Refinement Network for X-ray Coronary Angiogram

• URL: http://arxiv.org/abs/2510.27315v1
• Date: Fri, 31 Oct 2025 09:40:29 GMT
• Title: CASR-Net: An Image Processing-focused Deep Learning-based Coronary Artery Segmentation and Refinement Network for X-ray Coronary Angiogram
• Authors: Alvee Hassan, Rusab Sarmun, Muhammad E. H. Chowdhury, M. Murugappan, Md. Sakib Abrar Hossain, Sakib Mahmud, Abdulrahman Alqahtani, Sohaib Bassam Zoghoul, Amith Khandakar, Susu M. Zughaier, Somaya Al-Maadeed, Anwarul Hasan,
• Abstract summary: Coronary artery disease (CAD) is critical for reducing mortality and improving patient treatment planning.<n>We present the Coronary Artery and Refinement Network (CASR-Net), a three-stage pipeline comprising image preprocessing, segmentation, and refinement.
• Score: 9.788176765955534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Early detection of coronary artery disease (CAD) is critical for reducing mortality and improving patient treatment planning. While angiographic image analysis from X-rays is a common and cost-effective method for identifying cardiac abnormalities, including stenotic coronary arteries, poor image quality can significantly impede clinical diagnosis. We present the Coronary Artery Segmentation and Refinement Network (CASR-Net), a three-stage pipeline comprising image preprocessing, segmentation, and refinement. A novel multichannel preprocessing strategy combining CLAHE and an improved Ben Graham method provides incremental gains, increasing Dice Score Coefficient (DSC) by 0.31-0.89% and Intersection over Union (IoU) by 0.40-1.16% compared with using the techniques individually. The core innovation is a segmentation network built on a UNet with a DenseNet121 encoder and a Self-organized Operational Neural Network (Self-ONN) based decoder, which preserves the continuity of narrow and stenotic vessel branches. A final contour refinement module further suppresses false positives. Evaluated with 5-fold cross-validation on a combination of two public datasets that contain both healthy and stenotic arteries, CASR-Net outperformed several state-of-the-art models, achieving an IoU of 61.43%, a DSC of 76.10%, and clDice of 79.36%. These results highlight a robust approach to automated coronary artery segmentation, offering a valuable tool to support clinicians in diagnosis and treatment planning.

Related papers

• Anatomy Guided Coronary Artery Segmentation from CCTA Using Spatial Frequency Joint Modeling [10.73944733412427]
  We propose a coronary artery segmentation framework that integrates myocardial anatomical priors, structure aware feature encoding, and three dimensional wavelet inverse wavelet transformations.<n> Experimental results demonstrate that the proposed method achieves a Dice coefficient of 0.8082, Sensitivity of 0.7946, Precision of 0.8471, and an HD95 of 9.77 mm, outperforming several mainstream segmentation models.
  arXiv  Detail & Related papers  (2025-12-14T04:12:40Z)
• Neural Discrete Representation Learning for Sparse-View CBCT Reconstruction: From Algorithm Design to Prospective Multicenter Clinical Evaluation [64.42236775544579]
  Cone beam computed tomography (CBCT)-guided puncture has become an established approach for diagnosing and treating thoracic tumours.<n>DeepPriorCBCT is a three-stage deep learning framework that achieves diagnostic-grade reconstruction using only one-sixth of the conventional radiation dose.
  arXiv  Detail & Related papers  (2025-11-30T12:45:02Z)
• Cancer-Net PCa-MultiSeg: Multimodal Enhancement of Prostate Cancer Lesion Segmentation Using Synthetic Correlated Diffusion Imaging [55.62977326180104]
  Current deep learning approaches for prostate cancer lesion segmentation achieve limited performance.<n>We investigate synthetic correlated diffusion imaging (CDI$s$) as an enhancement to standard diffusion-based protocols.<n>Our results establish validated integration pathways for CDI$s$ as a practical drop-in enhancement for PCa lesion segmentation tasks.
  arXiv  Detail & Related papers  (2025-11-11T04:16:12Z)
• A Novel Attention-Augmented Wavelet YOLO System for Real-time Brain Vessel Segmentation on Transcranial Color-coded Doppler [49.03919553747297]
  We propose an AI-powered, real-time CoW auto-segmentation system capable of efficiently capturing cerebral arteries.<n>No prior studies have explored AI-driven cerebrovascular segmentation using Transcranial Color-coded Doppler (TCCD)<n>The proposed AAW-YOLO demonstrated strong performance in segmenting both ipsilateral and contralateral CoW vessels.
  arXiv  Detail & Related papers  (2025-08-19T14:41:22Z)
• Myocardial Region-guided Feature Aggregation Net for Automatic Coronary artery Segmentation and Stenosis Assessment using Coronary Computed Tomography Angiography [13.885760158090692]
  Myocardial Region-guided Feature Aggregation Net is a novel U-shaped dual-encoder architecture that integrates anatomical prior knowledge to enhance robustness in coronary artery segmentation.<n>Our framework incorporates three key innovations: (1) a Myocardial Region-guided Module that directs attention to coronary regions via bridging expansion and multi-scale feature fusion, (2) a Residual Feature Extraction Module that combines parallel spatial channel attention with residual blocks to enhance local-global feature discrimination, and (3) a Multi-scale Feature Fusion Module for adaptive aggregation of hierarchical vascular features.
  arXiv  Detail & Related papers  (2025-04-27T16:43:52Z)
• A topology-preserving three-stage framework for fully-connected coronary artery extraction [39.47027832777158]
  Coronary artery extraction is a crucial prerequisite for computer-aided diagnosis of coronary artery disease.<n>We propose a topology-preserving three-stage framework for fully-connected coronary artery extraction.<n>This framework includes vessel segmentation, centerline reconnection, and missing vessel reconstruction.
  arXiv  Detail & Related papers  (2025-04-02T11:04:44Z)
• AGFA-Net: Attention-Guided and Feature-Aggregated Network for Coronary Artery Segmentation using Computed Tomography Angiography [5.583495103569884]
  We propose an attention-guided, feature-aggregated 3D deep network (AGFA-Net) for coronary artery segmentation using CCTA images. AGFA-Net leverages attention mechanisms and feature refinement modules to capture salient features and enhance segmentation accuracy. Evaluation on a dataset comprising 1,000 CCTA scans demonstrates AGFA-Net's superior performance, achieving an average Dice coefficient similarity of 86.74% and a Hausdorff distance of 0.23 mm.
  arXiv  Detail & Related papers  (2024-06-13T01:04:47Z)
• A Deep Learning Model for Coronary Artery Segmentation and Quantitative Stenosis Detection in Angiographic Images [2.6879908098704544]
  We propose a novel deep learning-based method for the automatic segmentation of coronary arteries in angiographic images.<n>The model combines the MedSAM and VM-UNet architectures to achieve high-performance results.<n>The stenosis detection algorithm achieved a true positive rate (TPR) of 0.5867 and a positive predictive value (PPV) of 0.5911.
  arXiv  Detail & Related papers  (2024-06-01T16:45:33Z)
• Spatiotemporal Disentanglement of Arteriovenous Malformations in Digital Subtraction Angiography [37.44819725897024]
  The presented method aims to enhance Digital Subtraction Angiography (DSA) image series by highlighting critical information via automatic classification of vessels. The method was tested on clinical DSA images series and demonstrated efficient differentiation between arteries and veins.
  arXiv  Detail & Related papers  (2024-02-15T00:29:53Z)
• Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling [66.75096111651062]
  We created a large-scale dataset of 10,021 thoracic CTs with 157 labels. We applied an ensemble of 3D anatomy segmentation models to extract anatomical pseudo-labels. Our resulting segmentation models demonstrated remarkable performance on CXR.
  arXiv  Detail & Related papers  (2023-06-06T18:01:08Z)
• Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application [48.89674088331313]
  We systematically evaluate a Deep Learning (DL) method in a 3D medical image segmentation task. Our method is integrated into the radiosurgery treatment process and directly impacts the clinical workflow.
  arXiv  Detail & Related papers  (2021-08-21T16:15:40Z)
• Encoder-Decoder Architectures for Clinically Relevant Coronary Artery Segmentation [0.0]
  Coronary X-ray angiography is a crucial clinical procedure for the diagnosis and treatment of coronary artery disease. Previous approaches have used non-optimal segmentation criteria, leading to less useful results. We propose a line of efficient and high-performance segmentation models using a new decoder architecture, the EfficientUNet++.
  arXiv  Detail & Related papers  (2021-06-21T23:32:11Z)
• A new approach to extracting coronary arteries and detecting stenosis in invasive coronary angiograms [9.733630514873376]
  We aim to develop an automatic algorithm by deep learning to extract coronary arteries from ICAs. In this study, a multi-input and multi-scale (MIMS) U-Net with a two-stage recurrent training strategy was proposed for the automatic vessel segmentation. Experimental results demonstrated that the proposed method achieved an average Dice score of 0.8329, an average sensitivity of 0.8281, and an average specificity of 0.9979 in our dataset with 294 ICAs obtained from 73 patient.
  arXiv  Detail & Related papers  (2021-01-25T01:48:27Z)
• A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging [90.29017019187282]
  " 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset. Analyse of the submitted algorithms using technical and biological metrics was performed. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
  arXiv  Detail & Related papers  (2020-04-26T08:49:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.