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

Soichi Mita, Shumpei Takezaki, Ryoma Bise

TL;DR

VesselFusion, which is a diffusion model to extract the vessel centerline from 3D CT image, uses a coarse-to-fine representation of the centerline and a voting-based aggregation for a natural and stable extraction.

Abstract

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.

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

TL;DR

VesselFusion, which is a diffusion model to extract the vessel centerline from 3D CT image, uses a coarse-to-fine representation of the centerline and a voting-based aggregation for a natural and stable extraction.

Abstract

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.
Paper Structure (12 sections, 13 equations, 5 figures, 2 tables)

This paper contains 12 sections, 13 equations, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. VesselFusion
  4. Overview
  5. Coarse-to-Fine Coordinate Representation
  6. CT-Conditioned Diffusion Model
  7. Voting-Based Aggregation
  8. Experimental Results
  9. Experimental Setup
  10. Experimental Results
  11. Ablation Study
  12. Conclusion

Figures (5)

  • Figure 1: Overview of VesselFusion: A conditional diffusion model extracts a vessel centerline from a 3D CT image.
  • Figure 2: Stochastic generation from Gaussian distribution to a vessel distribution. VesselFusion infrequently generates unnatural structure (e.g., tears, loops, noise).
  • Figure 3: (a) Representation of centerline coordinates by a coarse-to-fine style. (b) Voting and aggregation of some estimations from different initial noises.
  • Figure 4: Qualitative comparison of vessel centerline extraction: (a) Baseline U-Net, (b) VesselFormer, (c) VesselFusion. Green: True positive (correctly extracted vessels). Red: False positive. Gray: Ground truth. Blue: Areas that were missed by the comparison method but were correctly detected by VesselFusion.
  • Figure 5: The effect of the number of samples $K$ in VesselFusion. The horizontal axis shows the number of samples $K$ used for voting, the left vertical axis shows the F1 score at $R=1$ (the higher the better), and the right vertical axis shows Betti-0 (the lower the better).