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Dual-Part Multi-Lateral Branched Network for Multi-Class Segmentation in Cardiovascular Catheterization Angiograms — arXiv:2609.04590

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.04590
License (as archived): CC0-1.0
Archived: 2026-10-16T22:26:30.717Z
SHA-256 of the archived text: 30c07d45d7f36ae1f3ca05d9b17f3738d9b1885b902bd13500ad512027015e0e
Stable URL of this page: https://metraipolis.com/documento/dc_fe44ebdb88ff

How to cite

Dual-Part Multi-Lateral Branched Network for Multi-Class Segmentation in Cardiovascular Catheterization Angiograms — arXiv:2609.04590 — arXiv (en), https://arxiv.org/abs/2609.04590 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fe44ebdb88ff (Metraipolis archive dc_fe44ebdb88ff, sha256 30c07d45d7f36ae1f3ca05d9b17f3738d9b1885b902bd13500ad512027015e0e)

BibTeX

@misc{dcfe44ebdb88ff,
  title = {Dual-Part Multi-Lateral Branched Network for Multi-Class Segmentation in Cardiovascular Catheterization Angiograms — arXiv:2609.04590},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.04590},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fe44ebdb88ff; sha256 30c07d45d7f36ae1f3ca05d9b17f3738d9b1885b902bd13500ad512027015e0e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.04590. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Catheterisation image processing requires segmentation models that are fast, accurate and explainable. While most of the existing studies usually focus on binary segmentation, there is a recent demand for simultaneous segmentation of multiple structures found in catheterization scenes. In this study, a dual-part MLBNet architecture is designed with multi-lateral encoder blocks and multi-head decoder branches for class-aware segmentation in cardiovascular catheterization scenes. Lateral branches in the encoder enables repeated feature extraction to learn diverse shared representations, while multiple decoder heads are used to introduce class-skewed branches that specialize in different structural properties in catheterization scenes. To analyze the performances of the dual-part MLBNet architecture, several multi-class segmentation angiogram data obtained during cardiovascular catheterization in phantom models, synthetic human-simulated aorta, and animal model are used for model training and evaluation. Results obtained showed the dual-part models could effectively separate guidewire, catheter, vessels and background pixels to their classes of memberships with high probability. The results demonstrate that all models were able to distinguish the dominant background class from foreground structures with high overall accuracy.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-07 in cs.AI).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI); Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.04590 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T22:18:11.511Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-07?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).