mlsanityData to 14 Oct 2026

Segmentation and Vascular Vectorization for Coronary Artery by Geometry-based Cascaded Neural Network

arXiv 2305.04208 ↗First posted 7 May 2023eess.IV cs.CVcited by 21

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Abstract (arXiv)

Segmentation of the coronary artery is an important task for the quantitative analysis of coronary computed tomography angiography (CCTA) images and is being stimulated by the field of deep learning. However, the complex structures with tiny and narrow branches of the coronary artery bring it a great challenge. Coupled with the medical image limitations of low resolution and poor contrast, fragmentations of segmented vessels frequently occur in the prediction. Therefore, a geometry-based cascaded segmentation method is proposed for the coronary artery, which has the following innovations: 1) Integrating geometric deformation networks, we design a cascaded network for segmenting the coronary artery and vectorizing results. The generated meshes of the coronary artery are continuous and accurate for twisted and sophisticated coronary artery structures, without fragmentations. 2) Different from mesh annotations generated by the traditional marching cube method from voxel-based labels, a finer vectorized mesh of the coronary artery is reconstructed with the regularized morphology. The novel mesh annotation benefits the geometry-based segmentation network, avoiding bifurcation adhesion and point cloud dispersion in intricate branches. 3) A dataset named CCA-200 is collected, consisting of 200 CCTA images with coronary artery disease. The ground truths of 200 cases are coronary internal diameter annotations by professional radiologists. Extensive experiments verify our method on our collected dataset CCA-200 and public ASOCA dataset, with a Dice of 0.778 on CCA-200 and 0.895 on ASOCA, showing superior results. Especially, our geometry-based model generates an accurate, intact and smooth coronary artery, devoid of any fragmentations of segmented vessels.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 18 of its 51 references that are in the map. First posted on arXiv 7 May 2023.

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18 of 51 references.

References · 18

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Inductive Representation Learning on Large Graphscited by 6,320
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  5. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
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  7. 2018Pixel2Mesh: Generating 3D Mesh Models from Single RGB Imagescited by 651
  8. 2020Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classificationcited by 506
  9. 2017Road Extraction by Deep Residual U-Netcited by 333
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6 more
  1. 2019Deep Distance Transform for Tubular Structure Segmentation in CT Scanscited by 52
  2. 2018Deep Vessel Segmentation By Learning Graphical Connectivitycited by 43
  3. 2019Graph Convolutional Networks for Coronary Artery Segmentation in Cardiac CT Angiographycited by 18
  4. 2023Graph Convolution Based Cross-Network Multi-Scale Feature Fusion for Deep Vessel Segmentationcited by 7
  5. 2022Computed tomography coronary angiogram images, annotations and associated data of normal and diseased arteriescited by 4
  6. 2020Coronary Wall Segmentation in CCTA Scans via a Hybrid Net with Contours Regularizationcited by 3

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Citing papers per year: 2024: 2; 2025: 5; 2026: 14.

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  10. 2026Stepwise Token Selection for Efficient Multimodal Large Language Modelscited by 5
  11. 2025DAM-Seg: Anatomically accurate cardiac segmentation using Dense Associative Networkscited by 3
  12. 2025Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutionscited by 2
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  3. 2026DiffPrune: differentiable information throttling for token pruning in vision-language modelscited by 1
  4. 2026ImageCAS-X: a dataset and benchmark for coronary artery segmentation and centerline extraction in coronary CT angiographycited by 1
  5. 2024Vascular Segmentation of Functional Ultrasound Images using Deep Learningcited by 0
  6. 2026From Snapshots to Symphonies: The Evolution of Protein Prediction from Static Structures to Generative Dynamics and Multimodal Interactionscited by 0
  7. 2026Autonomous Drift Learning in Data Streams: A Unified Perspectivecited by 0
  8. 2026MARVEL: Universal Murray's Law-informed Vessel Tree Segmentation and Topology Estimationcited by 0
  9. 2026StepPrune: Adaptive Sequential Visual Token Selection across Multimodal Large Language Modelscited by 0