VesselSDF: Distance Field Priors for Vascular Network Reconstruction

• URL: http://arxiv.org/abs/2506.16556v1
• Date: Thu, 19 Jun 2025 19:20:51 GMT
• Title: VesselSDF: Distance Field Priors for Vascular Network Reconstruction
• Authors: Salvatore Esposito, Daniel Rebain, Arno Onken, Changjian Li, Oisin Mac Aodha,
• Abstract summary: We present VesselSDF, a novel framework that leverages signed distance fields for robust vessel reconstruction.<n>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.<n>VesselSDF significantly outperforms existing methods and preserves vessel geometry and connectivity, enabling more reliable vascular analysis in clinical settings.
• Score: 19.422554836882515
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation [9.102738065373615]
  We propose a novel vessel segmentation framework called PASC Net.<n>It includes two key modules: a plug-and-play shape self-learning convolutional (SSL) module that optimize convolution kernel design, and a hierarchical topological constraint (HTC) module that ensures vascular connectivity through topological constraints.<n>When integrated into the nnUNet framework, our method outperformed other methods on multiple metrics, achieving state-of-the-art vascular segmentation performance.
  arXiv  Detail & Related papers  (2025-07-05T11:28:35Z)
• KLDD: Kalman Filter based Linear Deformable Diffusion Model in Retinal Image Segmentation [51.03868117057726]
  This paper proposes a novel Kalman filter based Linear Deformable Diffusion (KLDD) model for retinal vessel segmentation. Our model employs a diffusion process that iteratively refines the segmentation, leveraging the flexible receptive fields of deformable convolutions. Experiments are evaluated on retinal fundus image datasets (DRIVE, CHASE_DB1) and the 3mm and 6mm of the OCTA-500 dataset.
  arXiv  Detail & Related papers  (2024-09-19T14:21:38Z)
• RSF-Conv: Rotation-and-Scale Equivariant Fourier Parameterized Convolution for Retinal Vessel Segmentation [58.618797429661754]
  We propose a rotation-and-scale equivariant Fourier parameterized convolution (RSF-Conv) specifically for retinal vessel segmentation. As a general module, RSF-Conv can be integrated into existing networks in a plug-and-play manner. To demonstrate the effectiveness of RSF-Conv, we also apply RSF-Conv+U-Net and RSF-Conv+Iter-Net to retinal artery/vein classification.
  arXiv  Detail & Related papers  (2023-09-27T13:14:57Z)
• Pairwise-Constrained Implicit Functions for 3D Human Heart Modelling [60.56741715207466]
  We introduce a pairwise-constrained SDF approach that models the heart as a set of interdependent SDFs.<n>Our method significantly improves inner structure accuracy over single-SDF, UDF-based, voxel-based, and segmentation-based reconstructions.
  arXiv  Detail & Related papers  (2023-07-16T10:07:15Z)
• Morphology Edge Attention Network and Optimal Geometric Matching Connection model for vascular segmentation [3.6368619769561668]
  We propose a novel Morphology Edge Attention Network (MEA-Net) for the segmentation of vessel-like structures. We also present an Optimal Geometric Matching Connection (OGMC) model to connect the broken vessel segments. Our method achieves superior or competitive results compared to state-of-the-art methods on four datasets of 3D vascular segmentation tasks.
  arXiv  Detail & Related papers  (2023-06-02T01:52:35Z)
• Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation [48.638327652506284]
  Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. We present a novel approach, the affinity feature strengthening network (AFN), which jointly models geometry and refines pixel-wise segmentation features using a contrast-insensitive, multiscale affinity approach.
  arXiv  Detail & Related papers  (2022-11-12T05:39:17Z)
• Modeling and hexahedral meshing of cerebral arterial networks from centerlines [0.0]
  Centerline-based representation is widely used to model large vascular networks with small vessels. We propose an automatic method to generate a structured hexahedral mesh suitable for CFD directly from centerlines. We demonstrate the efficiency of our method by entirely meshing a dataset of 60 cerebral vascular networks.
  arXiv  Detail & Related papers  (2022-01-20T16:30:17Z)
• Deep Open Snake Tracker for Vessel Tracing [32.97987423431042]
  Vessel tracing by modeling vascular structures in 3D medical images with centerlines and radii can provide useful information for vascular health. Existing algorithms have been developed but there are certain persistent problems such as incomplete or inaccurate vessel tracing. We propose here a deep learning based open curve active contour model (DOST) to trace vessels in 3D images.
  arXiv  Detail & Related papers  (2021-07-19T17:59:31Z)
• FetReg: Placental Vessel Segmentation and Registration in Fetoscopy Challenge Dataset [57.30136148318641]
  Fetoscopy laser photocoagulation is a widely used procedure for the treatment of Twin-to-Twin Transfusion Syndrome (TTTS) This may lead to increased procedural time and incomplete ablation, resulting in persistent TTTS. Computer-assisted intervention may help overcome these challenges by expanding the fetoscopic field of view through video mosaicking and providing better visualization of the vessel network. We present a large-scale multi-centre dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms for the fetal environment with a focus on creating drift-free mosaics from long duration fetoscopy videos.
  arXiv  Detail & Related papers  (2021-06-10T17:14:27Z)
• Hierarchical Deep Network with Uncertainty-aware Semi-supervised Learning for Vessel Segmentation [58.45470500617549]
  We propose a hierarchical deep network where an attention mechanism localizes the low-contrast capillary regions guided by the whole vessels. The proposed method achieves the state-of-the-art performance in the benchmarks of both retinal artery/vein segmentation in fundus images and liver portal/hepatic vessel segmentation in CT images.
  arXiv  Detail & Related papers  (2021-05-31T06:55:43Z)

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.