arXiv:2605.26277v1 Announce Type: cross 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, curvatu
The proliferation of deep learning in medical imaging is encountering bottlenecks due to the scarcity of expertly annotated datasets, prompting research into synthetic data solutions like VesselSim.
This development could significantly accelerate progress in medical AI by removing a major hurdle for training advanced models, making sophisticated diagnostic tools more widely available.
The reliance on expensive and time-consuming expert annotations for 3D blood vessel segmentation could drastically decrease, enabling faster development and deployment of medical AI applications without human labeling.
- · Medical AI developers
- · Healthcare providers
- · Patients with vascular diseases
- · Medical imaging companies
- · Medical data annotation services dependent on manual labeling
- · Traditional deep learning approaches relying solely on real annotated data
More accurate and accessible AI models for vascular disease diagnosis and surgical planning become available.
Reduced healthcare costs associated with diagnosis and treatment planning due to automation and improved AI accuracy.
Enhanced global access to advanced medical imaging diagnostics, particularly in regions with limited medical expertise.
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Read at arXiv cs.AI