arXiv (All)
open access
UI-VISA: U-Net Initialized Vascular Image Segmentation Architecture
Accurate segmentation of vascular structures in digital subtraction angiography (DSA) images remains challenging due to the thin, elongated, and branching nature of blood vessels. Pixel-wise deep learning approaches such as U-Net achieve strong general-purpose segmentation performance but often prod
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Beyond Volume Overlap: Surface Matching for Topology-Aware Coronary Artery Segmentation#1115696
- Beyond In-Distribution Metrics: A Systematic Out-of-Distribution Evaluation of Congenital Heart Disease Segmentation#919459
- Deep Learning-based Medical Image Segmentation for Early Cancer Detection#246692
- Multi-stage deep learning architecture for carotid artery segmentation and stenosis evaluation: comparative study with digital subtraction angiography.#170626
- Automated Segmentation of Intracranial Arteries on 4D Flow MRI for Hemodynamic Quantification#424622
Record · ID 821619
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive