VesselSDF: Distance Field Priors for Vascular Network Reconstruction
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Abstract (arXiv)
Accurate segmentation of vascular networks from sparse CT scan slices remains a significant challenge in medical imaging, particularly due to the thin, branching nature of vessels and the inherent sparsity between imaging planes. Existing deep learning approaches, based on binary voxel classification, often struggle with structural continuity and geometric fidelity. To address this challenge, we present VesselSDF, a novel framework that leverages signed distance fields (SDFs) for robust vessel reconstruction. Our method reformulates vessel segmentation as a continuous SDF regression problem, where each point in the volume is represented by its signed distance to the nearest vessel surface. This continuous representation inherently captures the smooth, tubular geometry of blood vessels and their branching patterns. We obtain accurate vessel reconstructions while eliminating common SDF artifacts such as floating segments, thanks to our adaptive Gaussian regularizer which ensures smoothness in regions far from vessel surfaces while producing precise geometry near the surface boundaries. Our experimental results demonstrate that VesselSDF significantly outperforms existing methods and preserves vessel geometry and connectivity, enabling more reliable vascular analysis in clinical settings.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 22 of its 32 references that are in the map. First posted on arXiv 19 Jun 2025.
Builds on
22 of 32 references.
References · 22
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
- 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,558
- 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
- 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
- 2020Implicit Geometric Regularization for Learning Shapescited by 710
- 2021The Medical Segmentation Decathloncited by 628
- 2019UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentationcited by 586
- 2020clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentationcited by 294
- 2021nnFormer: Interleaved Transformer for Volumetric Segmentationcited by 180
- 2020Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfacescited by 147
- 2022UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentationcited by 112
10 more
- 2018DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumescited by 62
- 2024Centerline Boundary Dice Loss for Vascular Segmentationcited by 27
- 2024vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentationcited by 18
- 2022Going Off-Grid: Continuous Implicit Neural Representations for 3D Vascular Modelingcited by 16
- 2023Score-Based Generative Models for Medical Image Segmentation using Signed Distance Functionscited by 8
- 20233D Arterial Segmentation via Single 2D Projections and Depth Supervision in Contrast-Enhanced CT Imagescited by 4
- 2024Automating Vessel Segmentation in the Heart and Brain: A Trend to Develop Multi-Modality and Label-Efficient Deep Learning Techniquescited by 2
- 2024CrossSDF: 3D Reconstruction of Thin Structures From Cross-Sectionscited by 2
- 2022Slice Imputation: Intermediate Slice Interpolation for Anisotropic 3D Medical Image Segmentationcited by 1
- 2024Efficient Slice Anomaly Detection Network for 3D Brain MRI Volumecited by 1
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Citing papers per year: 2026: 2.
2 papers.