The study presents a fully automated 4D U‑Net that segments the ascending aorta, arch, and proximal descending aorta in 4D flow MRI using a hybrid 4D convolutional kernel and sparse 4D labels derived from 2D expert contours and centerlines. Trained on 268 scans from eight centers, the model achieved high Dice scores (0.927 internal, 0.911 external) and excellent agreement with expert measurements of peak velocity, net flow, wall shear stress, and diameters (ICC ≥0.954 internal, ≥0.980 external). Compared to frame‑wise 3D networks and semi‑automatic methods, the 4D U‑Net outperformed in diastole and generalised well to independent post‑contrast data.
arXiv:2606. 26764v1 Announce Type: cross Abstract: Developing robust artificial intelligence models for 4D (3D + time) medical imaging is constrained by limited annotated data, inter-device domain shifts, and privacy restrictions.
arXiv:2608. 12274v1 Announce Type: cross Abstract: Background: Accurate segmentation of the Left Anterior Descending (LAD) artery in 3D free-breathing, non-contrast CT is critical for cardiac dose sparing in thoracic radiotherapy.
The paper presents a deep learning super‑resolution framework for 4D flow MRI of carotid arteries, using convolutional block attention modules to focus on clinically relevant spatial features and reduce noise. Trained on 120 patients with 240 stenosed carotid arteries, the model leverages patient‑specific CFD simulations as high‑resolution ground truth. Results show a significant reduction in RMSE and improved reconstruction of complex flow patterns compared to a baseline without attention.
Developing robust artificial intelligence models for 4D (3D + time) medical imaging is constrained by limited annotated data, inter-device domain shifts, and privacy restrictions. To address this, we propose a 4D controllable generative framework for anatomically consistent data augmentation.
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.
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.
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.
arXiv:2608.23879v1 Announce Type: cross Abstract: Cardiac cine-MRI serves as a direct visual indicator of cardiovascular hemodynamics by capturing the continuous wall motion of the aorta. Quantifying...
arXiv:2608. 19965v1 Announce Type: cross Abstract: Segmentation of curvilinear anatomical structures in 3D medical images remains challenging due to complex topology, severe class imbalance, weak contrast, and large variations in structure morphology.
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...
arXiv:2608.29246v1 Announce Type: cross Abstract: Late gadolinium enhancement (LGE) cardiac magnetic resonance (MR) imaging is the modality of choice to assess myocardial infarction (MI) lesions. Now...
arXiv:2608. 19769v1 Announce Type: cross Abstract: Fast and accurate segmentation of Acute Ischemic Stroke (AIS) lesions is essential for stroke prognosis and treatment planning.