A review of segmentation methods in short axis cardiac MR images

By Caroline Petitjean, Jean-Nicolas Dacher. Medical Image Anal., 2011

756 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/3a63dfc85c7c5e703f4458f28506108087b113f5

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance (2017, methodology): “Further- ore, their analysis ( Petitjean and Dacher, 2011 ) of the MICCAI 009 challenge results ( Radau et al., 2009 ) indicates that imageased methodologies ( Lu et al., 2009; Huang et al., 2009; Uzunba ̧s t al., 2012 ) (e.g., thresholding, or dynamic programming applied o image segmentation…”
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (2015, methodology): “Thus, in addition to violating shape invariance (as with standard CINE MR), the principal assumption of appearance invariance (consistent intensity [12]) is violated in CP-BOLD MR as well.”
A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI (2015, methodology): “…imaging (MRI) is now routinely being used for the evaluation of the function and structure of the cardiovascular system (Yuan et al., 2002; Lima and Desai, 2004; Frangi et al., 2001; Petitjean and Dacher, 2011; Tavakoli and Amini, 2013; Heimann and Meinzer, 2009; Suinesiaputra et al., 2014).”
A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI (2016, methodology): “…some of the above technical difficulties to achieve incremental progress on automated ventricle segmentation from short-axis cine MRI, the resulting automated segmentation contours still need to be significantly improved in order to be useable in the clinical setting (Petitjean and Dacher, 2011).”
Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? (2018, neutral): “proposed in 2011 a complete review of segmentation methods for delineating the LV and/or the RV in short axis cardiac MR images [20].”
Multivariate Mixture Model for Myocardial Segmentation Combining Multi-Source Images (2016, methodology): “SEGMENTATION from medical images is an essential prerequisite in a number of imaging data assisted medical applications, such as in the localization and quantification of tissues and pathologies, and modeling of anatomical structures [1], [2], [3].”