VesselSim: learning 3D blood vessel segmentation without expert annotations

• URL: http://arxiv.org/abs/2605.26277v2
• Date: Wed, 27 May 2026 16:27:41 GMT
• Title: VesselSim: learning 3D blood vessel segmentation without expert annotations
• Abstract summary: We propose VesselSim, a two-stage framework for universal 3D blood vessel segmentation.<n>A 3D U-Net is trained solely on synthetic data to generate 16,500 anatomically plausible 3D angiographic volumes.<n>VesselSim achieves competitive performance with state-of-the-art segmentation foundation models.
• Score: 7.634402582162916
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Blood vessel segmentation is a core task in medical image analysis for the care of vascular diseases and surgical planning, yet the challenges of providing expert vascular annotations pose a major obstacle for the progress of related deep learning techniques. To address this, we propose VesselSim, a two-stage framework for universal 3D blood vessel segmentation that eliminates the need for real annotated data during training. First, we introduce a stochastic, geometry-driven vascular simulation framework that models recursive branching, curvature-controlled growth, and collision-aware topology, followed by domain-randomized intensity synthesis to generate 16,500 anatomically plausible 3D angiographic volumes. Second, a 3D U-Net is trained solely on this synthetic data. To bridge the domain gap from synthetic to real images at inference time, we introduce a test-time adaptation strategy via a self-supervised mask reconstruction decoder, enabling adaptation to unseen clinical scans without prior domain knowledge. We evaluate VesselSim in a zero-shot setting on multiple real-world datasets spanning MR and CT across several anatomical regions, including the brain and kidneys. Despite being trained exclusively on synthetic data, VesselSim achieves performance competitive with state-of-the-art vascular segmentation foundation models. These findings suggest that learning vessel geometry from synthetic tubular structures is effective for robust cross-domain generalization, substantially reducing the reliance on acquired medical imaging data and more importantly, expert annotations.

Related papers

• Anatomy-Grounded Synthetic Coronary Angiography for Geometry-Informed Multi-View Matching [6.620961894485603]
  We introduce a physically-grounded data generation framework that synthesizes high-fidelity Digital Reconstructed Radiographs (DRRs) from 3D Coronary CT Angiography (CCTA) volumes.<n>Our framework generates dense, highly accurate 3D-to-2D projection labels by simulating realistic C-arm acquisition geometry on patient anatomy at zero human cost.<n>Unlike real angiography where assessment relies on subjective human annotation, our dataset provides 2D correspondence labels with paired images, allowing human-free evaluation.
  arXiv  Detail & Related papers  (2026-06-26T14:58:23Z)
• VRXU-net: A Deep Learning Approach for Brain Ischemic Stroke Lesion Detection and Segmentation in T1W MRI [0.0]
  This study introduces a novel VRU-Net architecture, derived from visual features, residual connections, and a U-shaped network, for detecting and segmenting ischemic stroke lesions in 3D magnetic resonance imaging scans.<n>To improve both performance and processing speed, a high-performance classifier is applied before the segmentation model in a sequential framework.<n>The proposed model is trained on the Anatomical Tracings of Lesions After Stroke dataset and outperforms state-of-the-art models in terms of accuracy and Dice coefficient.
  arXiv  Detail & Related papers  (2026-05-20T18:44:08Z)
• Vesselpose: Vessel Graph Reconstruction from Learned Voxel-wise Direction Vectors in 3D Vascular Images [2.8991059698468287]
  We propose an approach to extract topologically more accurate vascular graphs from 3D image data.<n>Our approach achieves state-of-the-art performance on three benchmark datasets.
  arXiv  Detail & Related papers  (2026-05-01T09:34:39Z)
• TRELLIS-Enhanced Surface Features for Comprehensive Intracranial Aneurysm Analysis [2.624902795082451]
  Intracranial aneurysms pose a significant clinical risk yet are difficult to detect, delineate and model due to limited annotated 3D data.<n>We propose a cross-domain feature-transfer approach that leverages the latent geometric embeddings learned by TRELLIS, a generative model trained on large-scale non-medical 3D datasets.
  arXiv  Detail & Related papers  (2025-09-03T07:51:17Z)
• Hessian-Based Lightweight Neural Network HessNet for State-of-the-Art Brain Vessel Segmentation on a Minimal Training Dataset [39.407739937584104]
  We create a semi-manually annotated brain vessel dataset of brain MRA images based on the IXI dataset (annotated 200 images)<n>The dataset is available at https://git.scinalytics.com/terilat/VesselDatasetPartly.
  arXiv  Detail & Related papers  (2025-08-21T15:45:19Z)
• Semantic Segmentation for Preoperative Planning in Transcatheter Aortic Valve Replacement [61.573750959726475]
  We consider medical guidelines for preoperative planning of the transcatheter aortic valve replacement (TAVR) and identify tasks that may be supported via semantic segmentation models.<n>We first derive fine-grained TAVR-relevant pseudo-labels from coarse-grained anatomical information, in order to train segmentation models and quantify how well they are able to find these structures in the scans.
  arXiv  Detail & Related papers  (2025-07-22T13:24:45Z)
• Recursive Variational Autoencoders for 3D Blood Vessel Generative Modeling [0.7722830505405722]
  We developed a Recursive variational Neural Network (RvNN) that fully exploits the hierarchical organization of the vessel.<n>RvNN learns a low-dimensional manifold encoding branch connectivity along with geometry features describing the target surface.<n>We generate 3D models of blood vessels that are both accurate and diverse, which is crucial for medical and surgical training, hemodynamic simulations, and many other purposes.
  arXiv  Detail & Related papers  (2025-06-17T18:47:27Z)
• Leveraging Frequency Domain Learning in 3D Vessel Segmentation [50.54833091336862]
  In this study, we leverage Fourier domain learning as a substitute for multi-scale convolutional kernels in 3D hierarchical segmentation models. We show that our novel network achieves remarkable dice performance (84.37% on ASACA500 and 80.32% on ImageCAS) in tubular vessel segmentation tasks.
  arXiv  Detail & Related papers  (2024-01-11T19:07:58Z)
• Deep Learning for Vascular Segmentation and Applications in Phase Contrast Tomography Imaging [33.23991248643144]
  We present a thorough literature review, highlighting the state of machine learning techniques across diverse organs. Our goal is to provide a foundation on the topic and identify a robust baseline model for application to vascular segmentation in a new imaging modality. HiP CT enables 3D imaging of complete organs at an unprecedented resolution of ca. 20mm per voxel.
  arXiv  Detail & Related papers  (2023-11-22T11:15:38Z)
• A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning [56.52933974838905]
  Current medical workflow requires manual delineation of organs-at-risk (OAR) In this work, we aim to introduce a unified 3D pipeline for OAR localization-segmentation. Our proposed framework fully enables the exploitation of 3D context information inherent in medical imaging.
  arXiv  Detail & Related papers  (2022-03-01T17:08:41Z)
• Hepatic vessel segmentation based on 3Dswin-transformer with inductive biased multi-head self-attention [46.46365941681487]
  We propose a robust end-to-end vessel segmentation network called Indu BIased Multi-Head Attention Vessel Net. We introduce the voxel-wise embedding rather than patch-wise embedding to locate precise liver vessel voxels. On the other hand, we propose inductive biased multi-head self-attention which learns inductive biased relative positional embedding from absolute position embedding.
  arXiv  Detail & Related papers  (2021-11-05T10:17:08Z)
• Data-driven generation of plausible tissue geometries for realistic photoacoustic image synthesis [53.65837038435433]
  Photoacoustic tomography (PAT) has the potential to recover morphological and functional tissue properties. We propose a novel approach to PAT data simulation, which we refer to as "learning to simulate" We leverage the concept of Generative Adversarial Networks (GANs) trained on semantically annotated medical imaging data to generate plausible tissue geometries.
  arXiv  Detail & Related papers  (2021-03-29T11:30:18Z)
• Fed-Sim: Federated Simulation for Medical Imaging [131.56325440976207]
  We introduce a physics-driven generative approach that consists of two learnable neural modules. We show that our data synthesis framework improves the downstream segmentation performance on several datasets.
  arXiv  Detail & Related papers  (2020-09-01T19:17:46Z)

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.