Incorporation of prior knowledge about organ shape and location is key to improve
performance of image analysis approaches. In particular, priors can be useful in cases …

Cardiovascular disease remains an integral field on which new research in both the
biomedical and technological fields is based, as it remains the leading cause of mortality …

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 …

The technological development of Artificial Intelligence (AI) has grown rapidly in recent
years. The applications of AI to cardiovascular imaging are various and could improve the …

Purpose To determine if patient survival and mechanisms of right ventricular failure in
pulmonary hypertension could be predicted by using supervised machine learning of three …

In this study, we propose a fast and accurate method to automatically localize anatomical
landmarks in medical images. We employ a global-to-local localization approach using fully …

Automatic detection of anatomical landmarks is an important step for a wide range of
applications in medical image analysis. Manual annotation of landmarks is a tedious task …

One of the major challenges in anatomical landmark detection, based on deep neural
networks, is the limited availability of medical imaging data for network learning. To address …

To better retain the deep features of an image and solve the sparsity problem of the end-to-
end segmentation model, we propose a new deep convolutional network model for medical …

Abstract Machine Learning (ML) has been categorized as a branch of Artificial Intelligence
(AI) under the Computer Science domain wherein programmable machines imitate human …