Abstract Purpose of Review Anatomical segmentation has played a major role within clinical
cardiology. Novel techniques through artificial intelligence-based computer vision have …

Cardiac cine magnetic resonance imaging (MRI) continues to be recognized as an
established modality for non-invasive assessment of the function and structure of the …

Cardiac magnetic resonance imaging typically generates hundreds of images in each scan,
and manual delineation of structures from these scans is tedious and time-consuming …

The emergence of deep learning has considerably advanced the state-of-the-art in cardiac
magnetic resonance (CMR) segmentation. Many techniques have been proposed over the …

Deep learning has become the most widely used approach for cardiac image segmentation
in recent years. In this paper, we provide a review of over 100 cardiac image segmentation …

Recent advances in deep learning have shown the capability to accurately segment cardiac
structures in magnetic resonance images. However, while these models provide a good …

In this paper, we develop a 2D and 3D segmentation pipelines for fully automated cardiac
MR image segmentation using Deep Convolutional Neural Networks (CNN). Our models are …

Background: Convolutional neural network (CNN) based segmentation methods provide an
efficient and automated way for clinicians to assess the structure and function of the heart in …

Accurate segmentation of the heart is an important step towards evaluating cardiac function.
In this paper, we present a fully automated framework for segmentation of the left (LV) and …

Continuous advances in imaging technologies enable ever more comprehensive
phenotyping of human anatomy and physiology. Concomitant reduction of imaging costs …