TL;DR
Overall, MCP-Net provides a competitive and computationally efficient framework for automated multi-structure cardiac cine-MRI segmentation and external validation on the M&Ms benchmark without retraining or fine-tuning demonstrated the effectiveness and cross-dataset generalization capability of MCP-Net.
Accurate segmentation of cardiac structures in cine cardiac magnetic resonance (CMR) images is essential for quantitative assessment of cardiac function and disease. However, automatic delineation of the right ventricle, myocardium, and left ventricle remains challenging because of anatomical scale variations, weak tis...
Automated segmentation of the left and right ventricles (LVs and RVs) in cine cardiac MRI (CMR) underpins reliable volumetry and mass estimation. However, papillary muscles and trabeculae (PM/T) introduce clinically meaningful variability and exacerbate cross-dataset domain shift. We present VentrEX, an anatomically gu...
A fully supervised framework that couples a Vision Transformer encoder with a convolutional decoder to segment the left ventricle (LV), right ventricle (RV), and myocardium (Myo) on the Automated Cardiac Diagnosis Challenge (ACDC) dataset indicates that transformer-based encoders provide a competitive and interpretable...
To overcome the architectural mismatch inherent in existing hybrid networks, a novel Scaling Feature Pyramid (SFP) is proposed, which effectively bridges the single-scale 3D Vision Transformer (ViT) encoder and the multi-scale CNN decoder by transforming the ViT's output into a hierarchical feature pyramid, ensuring th...
Accurate and temporally coherent tracking of the cardiac structures throughout the entire systolic-diastolic cycle remains a fundamental unsolved problem in computational cardiology. Contemporary deep learning approaches for cardiac MRI segmentation are predominantly frame- independent, disregarding the abundant biomec...
Cardiovascular diseases remain a leading cause of mortality worldwide, and accurate segmentation of cardiac structures from MRI is critical for clinical diagnosis. We propose a hybrid framework that integrates a Vision Transformer encoder with a U-Net decoder for automated cardiac MRI segmentation and abnormality detec...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
