mlsanityData to 7 Oct 2026

VesselFusion: Diffusion Models for Vessel Centerline Extraction from 3D CT Images

arXiv 2603.08135 ↗First posted 9 Mar 2026cs.CVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Vessel centerline extraction from 3D CT images is an important task because it reduces annotation effort to build a model that estimates a vessel structure. It is challenging to estimate natural vessel structures since conventional approaches are deterministic models, which cannot capture a complex human structure. In this study, we propose VesselFusion, which is a diffusion model to extract the vessel centerline from 3D CT image. The proposed method uses a coarse-to-fine representation of the centerline and a voting-based aggregation for a natural and stable extraction. VesselFusion was evaluated on a publicly available CT image dataset and achieved higher extraction accuracy and a more natural result than conventional approaches.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 17 of its 28 references that are in the map. First posted on arXiv 9 Mar 2026.

Builds on

17 of 28 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer19 Jan
  2. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Survey14 Nov
  3. 2022MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model1 Nov

Other references · 14

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2020Denoising Diffusion Implicit Modelscited by 9,655
  5. 2022Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioningcited by 362
  6. 2022Relationformer: A Unified Framework for Image-to-Graph Generationcited by 67
  7. 2018DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumescited by 62
  8. 2022ImageCAS: A Large-Scale Dataset and Benchmark for Coronary Artery Segmentation based on Computed Tomography Angiography Imagescited by 54
  9. 2023VecFusion: Vector Font Generation with Diffusioncited by 20
  10. 2018Coronary Artery Centerline Extraction in Cardiac CT Angiography Using a CNN-Based Orientation Classifiercited by 16
  11. 2020Learning Hybrid Representations for Automatic 3D Vessel Centerline Extractioncited by 9
  12. 2025Trexplorer Super: Topologically Correct Centerline Tree Tracking of Tubular Objects in CT Volumescited by 4
2 more
  1. 2020Multi-Resolution 3D Convolutional Neural Networks for Automatic Coronary Centerline Extraction in Cardiac CT Angiography Scanscited by 3
  2. 2025Adaptive Pseudo Label Selection for Individual Unlabeled Data by Positive and Unlabeled Learningcited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation
  2. 20243D Vessel Graph Generation Using Denoising Diffusion
  3. 2025Angio-Diff: Learning a Self-Supervised Adversarial Diffusion Model for Angiographic Geometry Generation
  4. 2024DiffusionBlend: Learning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstruction
  5. 2025VasTSD: Learning 3D Vascular Tree-state Space Diffusion Model for Angiography Synthesis
  6. 2026Wavelet-Optimized Pseudo-3D Accelerated Diffusion Model for Truncated Computed Laminography
  7. 2025Leveraging Diffusion Model and Image Foundation Model for Improved Correspondence Matching in Coronary Angiography
  8. 2023Stage-by-stage Wavelet Optimization Refinement Diffusion Model for Sparse-View CT Reconstruction