angiography 1arrhythmogenic cardiomyopathy 1benchmark challenge 1cardiac imaging 1circle of willis segmentation 1deep learning 1multimodal data 1PET/MRI 1unsupervised clustering 1vascular imaging 1
From the 2 of 2 papers with an AI index.
12 citations
- Nantes UniversitéFR2 papers
- AGH University of KrakowPL1 paper
- Athinoula A. Martinos Center for Biomedical ImagingUS1 paper
- Beijing Academy of Artificial IntelligenceCN1 paper
- Canon (United States)US1 paper
- Center for Excellence in Brain Science and Intelligence TechnologyCN1 paper
- Centre de Recherche en Cancérologie et Immunologie Intégrée Nantes AngersFR1 paper
- Centre Hospitalier de l’Université de MontréalCA1 paper
- Charité - Universitätsmedizin BerlinDE1 paper
- Chinese Academy of SciencesCN1 paper
- Cornell UniversityUS1 paper
- Duke Medical CenterUS1 paper
2 papers
cs.CV2026
A novel unsupervised machine learning strategy to handle multimodal cardiac PET/MRI data
Brunnhilde Ponsi, Thomas Carlier, Lara Marteau +5
The paper presents an unsupervised clustering pipeline that integrates PET and MRI cardiac images to automatically detect abnormal myocardial regions in patients with arrhythmogeni…
cs.CV2026★ 12 cited
The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography
Kaiyuan Yang, Fabio Musio, Yihui Ma +112
The paper introduces the TopCoW Challenge, a benchmark for automatically segmenting the Circle of Willis in CT and MR angiography using deep learning, and provides a new annotated…