Visual recognition of cardiac pathology based on 3D parametric model reconstruction
Author:
Publisher
Zhejiang University Press
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing
Link
https://link.springer.com/content/pdf/10.1631/FITEE.2200102.pdf
Reference36 articles.
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2. Attar R, Pereañez M, Bowles C, et al., 2019. 3D cardiac shape prediction with deep neural networks: simultaneous use of images and patient metadata. Proc 22nd Int Conf on Medical Image Computing and Computer-Assisted Intervention, p.586–594. https://doi.org/10.1007/978-3-030-32245-8_65
3. Bai WJ, Oktay O, Rueckert D, 2016. Classification of myocardial infarcted patients by combining shape and motion features. Proc 6th Int Workshop on Statistical Atlases and Computational Models of the Heart, p.140–145. https://doi.org/10.1007/978-3-319-28712-6_15
4. Bernard O, Lalande A, Zotti C, et al., 2018. Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans Med Imag, 37(11):2514–2525. https://doi.org/10.1109/TMI.2018.2837502
5. Bernardino G, Benkarim O, Sanz-de la Garza M, et al., 2020. Handling confounding variables in statistical shape analysis-application to cardiac remodelling. Med Image Anal, 65:101792. https://doi.org/10.1016/j.media.2020.101792
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