Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI

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Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation (2022, background): “CoraNet, Shi et al. [87] 2D/3D CT, MRI Pancreas CT [130], MR Endo-cardium [167], ACDC [22] Conservative-radical”
Deep Learning for Cardiac Image Segmentation: A Review (2019, neutral): “York (Andreopoulos and Tsotsos, 2008) 2008 cine MRI 33 LV cardiomyopathy, aortic regurgitation, enlarged ventricles and ischemia Sunnybrook (Radau and Others, 2009) 2009 cine MRI 45 LV hypertrophy, heart failure w.”
50 Years of object recognition: Directions forward (2013, methodology): “Many of the models used for representing objects are also effectively employed by the medical imaging community for the robust segmentation of anatomical structures such as the brain and the heart ventricles [11, 12].”
Four-Chamber Heart Modeling and Automatic Segmentation for 3-D Cardiac CT Volumes Using Marginal Space Learning and Steerable Features (2008, neutral): “models (AAM) [21], [22], and deformable models [12], [17], [23]–[26].”
Statistical shape models for 3D medical image segmentation: A review (2009, methodology): “Originally presented only for 2D AAMs, the algorithm was later extended to 3D by Andreopoulos and Tsotsos (2008). Beichel et al.”