Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography
By O. Bernard, J. Bosch, B. Heyde, M. Alessandrini, D. Barbosa, S. Camarasu-Pop, F. Cervenansky, S. Valette, O. Mirea, M. Bernier and 21 more. 2016
104 citations recorded by the source corpus.
Subfields: Cardiology and Cardiovascular Medicine, Surgery, Medical Imaging
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cardiology and Cardiovascular Medicine
In Medical Imaging
- Daniel Rueckert
- Ben Glocker
- Konstantinos Kamnitsas
- Nassir Navab
- Seyed-Ahmad Ahmadi
- Fausto Milletarì
- Bernhard Kainz
- Enzo Ferrante
Citations in the corpus
- V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation (2016, methodology): “Automatic delineation of organs and structures of interest is often necessary to perform tasks such as visual augmentation [13], computer assisted diagnosis [15], interventions [24] and extraction of quantitative indices from images [1].”
- Deep Learning for Cardiac Image Segmentation: A Review (2019, unknown): “Cardiac image segmentation datasets: ACDC, Automated Cardiac Diagnosis Challenge; CETUS, Challenge on Endocardial Three-dimensional Ultrasound Segmentation;MM-WHS,Multi-ModalityWhole Heart Segmentation; LASC, Left Atrium Segmentation Challenge; LVSC, Left Ventricle Segmentation Challenge; RVSC, Righ”
- Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation (2017, background): “The challenge results can be found in [8].”