The combination of computational models and biophysical simulations can help to interpret
an array of experimental data and contribute to the understanding, diagnosis and treatment …

Recently, deep convolutional neural networks have achieved great success for medical
image segmentation. However, unlike segmentation of natural images, most medical images …

Semantic segmentation of medical images aims to associate a pixel with a label in a medical
image without human initialization. The success of semantic segmentation algorithms is …

A whole heart segmentation (WHS) method is presented for cardiac MRI. This segmentation
method employs multi-modality atlases from MRI and CT and adopts a new label fusion …

Knowledge of left atrial (LA) anatomy is important for atrial fibrillation ablation guidance,
fibrosis quantification and biophysical modelling. Segmentation of the LA from Magnetic …

Automated construction of surface geometries of cardiac structures from volumetric medical
images is important for a number of clinical applications. While deep-learning-based …

Diagnosis of ascending thoracic aortic aneurysm (ATAA) is based on the measurement of
the maximum aortic diameter, but size is not a good predictor of the risk of adverse events …

In this paper, we propose a novel heart segmentation pipeline Combining Faster R-CNN
and U-net Network (CFUN). Due to Faster R-CNN's precise localization ability and U-net's …

This review article gives a comprehensive survey of the progress made in computational
modeling of the human atria during the last 10 years. Modeling the anatomy has emerged …

Whole heart segmentation from magnetic resonance imaging or computed tomography is a
prerequisite for many clinical applications. Since manual delineation can be tedious and …