Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation
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 …
performance of image analysis approaches. In particular, priors can be useful in cases …
The applications of artificial intelligence in cardiovascular magnetic resonance—a comprehensive review
A Argentiero, G Muscogiuri, MG Rabbat… - Journal of Clinical …, 2022 - mdpi.com
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 …
biomedical and technological fields is based, as it remains the leading cause of mortality …
An exploration of 2D and 3D deep learning techniques for cardiac MR image segmentation
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 …
In this paper, we present a fully automated framework for segmentation of the left (LV) and …
Artificial intelligence in cardiovascular CT and MR imaging
LRM Lanzafame, GM Bucolo, G Muscogiuri, S Sironi… - Life, 2023 - mdpi.com
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 …
years. The applications of AI to cardiovascular imaging are various and could improve the …
Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study
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 …
pulmonary hypertension could be predicted by using supervised machine learning of three …
Deep learning-based regression and classification for automatic landmark localization in medical images
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 …
landmarks in medical images. We employ a global-to-local localization approach using fully …
Evaluating reinforcement learning agents for anatomical landmark detection
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 …
applications in medical image analysis. Manual annotation of landmarks is a tedious task …
Detecting anatomical landmarks from limited medical imaging data using two-stage task-oriented deep neural networks
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 …
networks, is the limited availability of medical imaging data for network learning. To address …
MC-Net: Multiple max-pooling integration module and cross multi-scale deconvolution network
H You, L Yu, S Tian, X Ma, Y Xing, N Xin… - Knowledge-Based Systems, 2021 - Elsevier
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 …
end segmentation model, we propose a new deep convolutional network model for medical …
Appositeness of optimized and reliable machine learning for healthcare: a survey
Abstract Machine Learning (ML) has been categorized as a branch of Artificial Intelligence
(AI) under the Computer Science domain wherein programmable machines imitate human …
(AI) under the Computer Science domain wherein programmable machines imitate human …