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arXiv Computer Vision

MoE-based Feature Adapter for Prompt-free Binary Coronary Artery Segmentation in X-ray Angiography

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Jun 29

HTC-SGA Former: A Hybrid Transformer-CNN Network with Self-Guided Attention and a New Boundary-Weighted Adaptive Loss for Coronary DSA Vessel Segmentation

Accurate coronary Digital Subtraction Angiography (DSA) vessel segmentation is essential for computer-aided diagnosis and treatment planning of coronary artery disease (CAD). However, thin low-contrast vessels, background interference, and severe vessel-background class imbalance make reliable segmentation of weak distal branches and vessel boundaries challenging.

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arXiv Machine Learning
Jul 15

The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography

arXiv:2312. 17670v5 Announce Type: replace-cross Abstract: The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain.

By Kaiyuan Yang, Fabio Musio, Yihui Ma, Norman Juchler, Johannes C. Paetzold, Rami Al-Maskari, Luciano H\"oher, Hongwei Bran Li, Ibrahim Ethem Hamamci, Anjany Sekuboyina, Suprosanna Shit, Houjing Huang, Chinmay Prabhakar, Ezequiel de la Rosa, Bastian Wittmann, Diana Waldmannstetter, Florian Kofler, Fernando Navarro, Martin J. Menten, Ivan Ezhov, Daniel Rueckert, Iris N. Vos, Ynte M. Ruigrok, Birgitta K. Velthuis, Hugo J. Kuijf, Pengcheng Shi, Wei Liu, Ting Ma, Maximilian R. Rokuss, Yannick Kirchhoff, Fabian Isensee, Klaus Maier-Hein, Chengcheng Zhu, Huilin Zhao, Philippe Bijlenga, Julien H\"ammerli, Catherine Wurster, Laura Westphal, Jeroen Bisschop, Elisa Colombo, Hakim Baazaoui, Hannah-Lea Handelsmann, Andrew Makmur, James Hallinan, Amrish Soundararajan, Benedikt Wiestler, Jan S. Kirschke, Evamaria O. Riedel, Roland Wiest, Emmanuel Montagnon, Laurent Letourneau-Guillon, Kwanseok Oh, Dahye Lee, Orhun Utku Aydin, Adam Hilbert, Jana Rieger, Dimitrios Rallios, Satoru Tanioka, Alexander Koch, Dietmar Frey, Abdul Qayyum, Moona Mazher, Steven Niederer, Nico Disch, Julius C. Holzschuh, Dominic LaBella, Francesco Galati, Daniele Falcetta, Maria A. Zuluaga, Chaolong Lin, Haoran Zhao, Zehan Zhang, Minghui Zhang, Xin You, Hanxiao Zhang, Guang-Zhong Yang, Yun Gu, Sinyoung Ra, Jongyun Hwang, Hyunjin Park, Junqiang Chen, Marek Wodzinski, Henning M\"uller, Nesrin Mansouri, Florent Autrusseau, Cansu Yalcin, Rachika E. Hamadache, Clara Lisazo, Joaquim Salvi, Adri\`a Casamitjana, Xavier Llad\'o, Uma Maria Lal-Trehan Estrada, Valeriia Abramova, Luca Giancardo, Arnau Oliver, Paula Casademunt, Adrian Galdran, Matteo Delucchi, Oscar Camara, Jialu Liu, Haibin Huang, Yue Cui, Zehang Lin, Yusheng Liu, Shunzhi Zhu, Tatsat R. Patel, Adnan H. Siddiqui, Vincent M. Tutino, Maysam Orouskhani, Huayu Wang, Mahmud Mossa-Basha, Yuki Sato, Sven Hirsch, Susanne Wegener, Bjoern Menze
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arXiv Machine Learning
Jul 27

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography

arXiv:2607. 22139v1 Announce Type: cross Abstract: Accurate pixel-level classification of coronary angiograms is critical for cardiovascular disease assessment, yet the field lacks standardized evaluation protocols.

By Dominik Bernard Lau, Hubert Malinowski, Jerzy Szyjut, Adam Brzeski, Tomasz Dziubich, Rados{\l}aw Targo\'nski, Tomasz Figatowski, Natalia Zieli\'nska
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arXiv AI
Sep 7

Dual-Part Multi-Lateral Branched Network for Multi-Class Segmentation in Cardiovascular Catheterization Angiograms

The paper introduces Dual-Part MLBNet, a multi-lateral branched network designed for fast, accurate, and explainable multi-class segmentation of cardiovascular catheterization angiograms. The architecture uses multi-lateral encoder blocks for diverse feature extraction and multi-head decoder branches that specialize in different structural properties, enabling simultaneous segmentation of guidewire, catheter, vessels, and background. Experiments on phantom, synthetic human-simulated aorta, and animal model angiograms demonstrate high overall accuracy and effective separation of foreground structures from background.

By Olatunji Omisore, Ahmed Elazab, Ali Shahidinejad, Fariza Sabrina
computer-vision
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arXiv Computer Vision
4d ago

Vascular anatomy-aware self-supervised pre-training for X-ray angiogram analysis

The paper introduces VasoMIM, a vascular anatomy‑aware masked image modeling framework for X‑ray angiogram analysis, which uses an anatomy‑guided masking strategy and an anatomical consistency loss to learn robust vascular representations. It also presents XA‑170K, the largest pre‑training dataset for X‑ray angiograms. Experiments on six datasets and four downstream tasks show that VasoMIM outperforms existing methods and achieves state‑of‑the‑art performance.

By De-Xing Huang, Chaohui Yu, Xiao-Hu Zhou, Tian-Yu Xiang, Qin-Yi Zhang, Mei-Jiang Gui, Rui-Ze Ma, Chen-Yu Wang, Nu-Fang Xiao, Fan Wang, Zeng-Guang Hou
benchmarks
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Hugging Face Trending Papers
Jul 7

MAC-XA: Multi-view Anatomy-Correspondence Fusion for Coronary Stenosis Reporting from X-ray Angiography

Multi-view reasoning in coronary X-ray angiography is inherently a cross-projection geometric problem, yet automated report generation in this setting remains largely unexplored. The 3D vascular topology leads to projection-dependent branch overlap and foreshortening, rendering single-view modeling fundamentally incomplete and unstable for lesion localization and stenosis grading.

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arXiv AI
Sep 1

ImageCAS-X: a dataset and benchmark for coronary artery segmentation and centerline extraction in coronary CT angiography

arXiv:2608.30404v1 Announce Type: cross Abstract: Accurate segmentation of the coronary vessel lumen is a prerequisite for quantitative assessment of atherosclerotic plaque and perivascular adipose t...

By Kit M. Bransby, Esther {\O}ksnebjerg, Kristoffer Kj{\ae}r, Jacob Kirkeby, Yasmin El Youssef, A\"ida Jim\'enez, Philip R. Pedersson, Martina C. de Knegt, Klaus F. Kofoed, Rasmus R. Paulsen
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arXiv Computer Vision
Oct 8

PPCAR-Net: Projection-Refined Parametric 3D Coronary Artery Reconstruction from Sparse X-ray Angiographic Views

PPCAR-Net is a neural network that reconstructs 3‑D coronary artery centrelines and radii from a sparse set of X‑ray angiographic views. It predicts a branch‑structured centreline-and‑radius representation directly, avoiding explicit point matching or triangulation. The method refines geometry and radius estimates using projection‑guided refiners and achieves real‑time performance with strong centreline accuracy, especially for the right coronary artery.

By Yu Ren, Hwee Kuan Lee, Tat-Jen Cham, Jonathan Yap, Khung Keong Yeo
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arXiv Computer Vision
Sep 4

Explainable Convolutional Neural Networks for Retinal Fundus Classification and Cutting-Edge Segmentation Models for Retinal Blood Vessels from Fundus Images

The paper presents a two‑pipeline framework for retinal fundus analysis that combines four‑class disease classification with vessel segmentation. It fine‑tunes eight ImageNet‑pretrained CNNs on the FIVES dataset, applies five gradient‑based explanation methods to assess model interpretability, and benchmarks ten U‑Net variants—including transformer‑based and attention‑enhanced architectures—on the FIVES and DRIVE datasets. The best classification results come from ResNet101 (94.17% accuracy), while the strongest segmentation performance is achieved by Attention U‑Net with a ResNet101V2 backbone, improving DRIVE IoU from 60.80% to 64.83%.

By Fatema Tuj Johora Faria, Mukaffi Bin Moin, Pronay Debnath, Asif Iftekher Fahim, Faisal Muhammad Shah
llmscomputer-visionfine-tuning
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