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
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: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:2608.30975v1 Announce Type: cross
Abstract: Cardiac magnetic resonance imaging (CMR) produces rich sequential data such as temporal cine videos and spatial LGE/mapping stacks, yet most deep lea...
By Athira J. Jacob, Puneet Sharma, Dorin Comaniciu, Daniel Rueckert
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: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
arXiv:2608. 20305v1 Announce Type: new Abstract: Myocardial scar segmentation from single-stack late gadolinium-enhanced cardiac magnetic resonance (LGE-CMR) imaging has been a longstanding and clinically important challenge, particularly in the presence of low tissue contrast, diffuse, and small scar regions.
By Nivetha Jayakumar, Hannah Kim, Amit R. Patel, Miaomiao Zhang
arXiv:2610.00279v1 Announce Type: new
Abstract: The segmentation of anatomical structures in medical images and particularly in MRI scans, is essential for clinical diagnosis and monitoring disease p...
By Eirini Cholopoulou, Dimitrios E. Diamantis, Dimitris K. Iakovidis
ORION‑CMR is a scanner‑native, end‑to‑end foundation model for cardiac MRI that performs sequence classification, ventricular function assessment, LGE detection, disease classification, and generates reports in about 90 seconds. Trained on 12.9 million images, it outperformed supervised baselines and a prior CMR foundation model, achieving state‑of‑the‑art LGE classification and scar segmentation. In a multi‑vendor clinical cohort, it reached an AUC of 0.96 for normal‑vs‑abnormal detection and 0.88 for multiclass disease classification, with generated reports agreeing 81.4% with expert interpretation.
By Omer Burak Demirel, Kelly K. Horst, Alessio Perazzolo, Elisa Bruno, Kenan Kaya, Rongzhen Ouyang, Enas Ahmed, Jouke Smink, Spencer L. Waddle, Zainudeen Kallumpurath, Tzu Cheng Chao, Dinghui Wang, Steve G. Langer, Timothy L. Kline, Panagiotis Korfiatis, Jacinta Browne, Ivana Isgum, Tim Leiner
arXiv:2602.20773v2 Announce Type: replace
Abstract: Purpose: Developing generalizable medical image segmentation models is challenging because imaging data are distributed across institutions and dif...
By Sachin Dudda Nagaraju, Ashkan Moradi, Bendik Skarre Abrahamsen, Mattijs Elschot
arXiv:2608.31073v1 Announce Type: new
Abstract: Whole-heart segmentation (WHS) in computed tomography (CT) and magnetic resonance imaging (MRI) is affected by acquisition shifts and heterogeneous car...
By Jiacheng Wang, Ivana Isgum, Ipek Oguz
arXiv:2607. 13936v1 Announce Type: cross Abstract: Arrhythmogenic left ventricular cardiomyopathy is a genetic myocardial disease difficult to diagnose due to the lack of gold standard criteria.
By Brunnhilde Ponsi (Nantes Universit\'e, CHU Nantes, Nantes, France, CRCI2NA, INSERM UMR 1307, Nantes, France), Thomas Carlier (Nantes Universit\'e, CHU Nantes, Nantes, France, CRCI2NA, INSERM UMR 1307, Nantes, France), Lara Marteau (Nantes Universit\'e, CHU Nantes, Nantes, France, Cardiology Department, INSERM UMR 1307, CIC 1413, l'institut du Thorax, Nantes, France), Aur\'elien Monnet (Siemens Healthineers France, Courbevoie, France), Thomas Eug\`ene (Nantes Universit\'e, CHU Nantes, Nantes, France, CRCI2NA, INSERM UMR 1307, Nantes, France), Jean-Michel Serfaty (Nantes Universit\'e, CHU Nantes, Nantes, France, Radiology Department, l'institut du Thorax, Nantes, France), Nicolas Piriou (Nantes Universit\'e, CHU Nantes, Nantes, France, Cardiology Department, INSERM UMR 1307, CIC 1413, l'institut du Thorax, Nantes, France), Hatem Necib (Nantes Universit\'e, CHU Nantes, Nantes, France, CRCI2NA, INSERM UMR 1307, Nantes, France)