arXiv Computer Vision By Olivier Bernard, William A. Romero R., Cyprien Bouton, Celia Goujat, Hang Jung Ling, Pierre-Marc Jodoin, Fumin Guo, Calder Sheagren, Graham Wright, Abdul Qayyum, Moona Mazher, Steven A. Niederer, Hairui Wang, Xiaomei Wu, Franz Thaler, Gernot Plank, Martin Urschler, Ricardo M. Rosales, Esther Pueyo, Nicolas Duchateau, Frederic Cervenansky, Patrick Clarysse, Loic Belle, Thomas Bochaton, Nathan Mewton, Magalie Viallon, Pierre Croisille

The MYOSAIQ Challenge: Myocardial Segmentation with Automated Infarct Quantification

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arXiv Computer Vision
Sep 2

CMRVision: A Foundation Model for Cardiac MR Image Analysis

CMRVision is a cardiac magnetic resonance (CMR) foundation model trained with DINOv3-style self‑supervised learning on 36 million multi‑center, multi‑sequence CMR images. It outperforms prior natural‑image, medical‑image, supervised, and CMR baselines on multi‑task segmentation (cine, LGE, mapping) and cine view classification, achieving Dice scores of 0.940–0.967 for LV and 0.855–0.905 for myocardium, and a zero‑shot Dice of 0.692 on unseen LGE long‑axis views. The model demonstrates robust cross‑view generalization and highest average accuracy (0.906) for cine view classification.

By Athira J. Jacob, Puneet Sharma, Daniel Rueckert
arXiv Computer Vision
Sep 14

SV-Cine: Diagnosis-Conditioned Segmentation of Single Ventricle Physiology via Generative Data Augmentation

The paper introduces SV-Cine, a cardiac MRI segmentation framework tailored for single ventricle physiology (SVP). It combines a generative data augmentation pipeline that creates synthetic 3D cardiac meshes and MRI, with a diagnosis-conditioned adaptation of the CineMA foundation model that uses patient-level diagnostic information to improve segmentation. Evaluations on an internal cohort show high Dice scores for left and right ventricles, outperforming nnU-Net, and demonstrate that incorporating diagnosis priors can adapt a pretrained model to specialized SVP tasks.

By Lila Cunge, Yuehong Liu, Hang Xu, Thomas Coudert, Pierangelo Renella, J Paul Finn, William Hsu, Kim-Lien Nguyen
arXiv AI
Jul 14

A Unified Framework for Comprehensive Cardiac CT Segmentation and Phenotyping: Human-in-the-Loop Data Annotation, Vision Foundation Model Development, Multicenter Evaluation and Clinical Validation

arXiv:2607. 11287v1 Announce Type: cross Abstract: Comprehensive quantification of cardiac structures from computed tomography (CT) remains limited not by data availability but by the scalability of measurements, which makes routine use impractical.

By Pooya Mohammadi Kazaj, Leo Fridolin Weber, Wen Xie, Seyed Amir Ahmad Safavi-Naini, Anselm Stark, Giovanni Baj, Ali Mokhtari, Toshiya Yoshida, Christoph Ryffel, Taishi Okuno, Yoshihiro Akashi, Ronny R. Buechel, Thomas Pilgrim, Waldo Valenzuela, George C. M. Siontis, Xiaowei Xu, Moritz Hundertmark, Stephan Windecker, Christoph Grani, Isaac Shiri
arXiv AI
Jun 9

Comparative evaluation of training strategies using partially labelled datasets for segmentation of white matter hyperintensities and stroke lesions in FLAIR MRI

arXiv:2601. 20503v2 Announce Type: replace-cross Abstract: White matter hyperintensities (WMH) and ischaemic stroke lesions (ISL) are key imaging biomarkers of cerebral small vessel disease (SVD) detectable on magnetic resonance imaging (MRI).

By Jesse Phitidis, Alison Q. Smithard, William N. Whiteley, Joanna M. Wardlaw, Miguel O. Bernabeu, Maria Vald\'es Hern\'andez
arXiv AI
Jun 2

CardioLens: Revealing the Clinical Reality Gap of MLLMs via Multi-Sequence Cardiac MRI Evaluations

arXiv:2606. 00123v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have shown strong performance on public medical benchmarks, yet existing evaluations often remain weak proxies for clinical use, relying on isolated inputs and simplified recognition-style tasks.

By Zixian Su, Hongkai Zhang, Fan Gao, Encheng Su, Taiping Qu, Jingwei Guo, Nan Zhang, Hui Wang, Zhen Zhou, Kairui Bo, Yan Chen, Yue Ren, Shuai Li, Lei Xu, Henggui Zhang