Statistical shape models for 3D medical image segmentation: A review

By Tobias Heimann, Hans-Peter Meinzer. Medical Image Anal., 2009

1,542 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7f9de58f8d20e11a303973adf0729ee5b632044f

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Citations in the corpus

Multi-Scale Self-Guided Attention for Medical Image Segmentation (2019, background): “Even though segmentation of medical images has been widely studied in the past [28], [29] it is undeniable that CNNs are driving progress in this field, leading to outstanding perfor-”
Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation (2019, methodology): “age segmentation mainly focused on using thresholding [34], statistical shape models [35] and machine learning [36–40], while recent ones are mainly deep-learning-based [41–43].”
Deformable medical image registration: setting the state of the art with discrete methods. (2011, background): “This is often an issue in applications where large organ variability is expected such as longitudinal abdominal imaging for radiation therapy (52) or population analysis for the creation of deformable, soft-tissue organ models (53).”
Fully Automatic Segmentation of the Prostate using Active Appearance Models (2012, background): “Variants of AAMs have been extensively developed (see [3] for a review).”
Medical image processing on the GPU - Past, present and future (2013, methodology): “graph cuts (Shi and Malik, 2000), segmentation by registration to templates or atlases and segmentation based on (statistical) shape models (Cootes et al., 1995; Heimann and Meinzer, 2009).”
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).”
Reducing the Hausdorff Distance in Medical Image Segmentation With Convolutional Neural Networks (2019, background): “mentation performance, segmentation algorithms rarely aim at minimizing or reducing HD directly [8], [9].”
Machine Learning and Radiology (2012, background): “More discussions on statistical shape models for 3D medical image segmentation can be found in a review paper (Heimann and Meinzer, 2009).”
Segmentation of Both Diseased and Healthy Skin From Clinical Photographs in a Primary Care Setting (2018, background): “gmentation, relying at least in part on the data being pre-localized by the photographer [9], [10]. Segmentation, both within and outside the ﬁeld of medical imaging, is a large and active ﬁeld [11], [12], [13], too broad to comprehensively capture here. However, most recent approaches involve deep ”
Automatic Multi-organ Segmentation on Abdominal CT with Dense V-networks (2018, neutral): “1) Common Multi-Organ Segmentation Methodologies: Statistical models [5], [6] involve co-registering images in a training data set to estimate anatomical correspondences, constructing a statistical model of the distribution of shapes [22]”
A review of segmentation methods in short axis cardiac MR images (2011, neutral): “, 2009; Huang et al., 2009). However these techniques require user interaction and cannot assess the ventricular surface in all phases, contrary to O’Brien et al. (2009), Casta et al.”
A Bayesian model of shape and appearance for subcortical brain segmentation (2011, positive): “…formulation, if the dimensionality of the shape representation exceeds the size of the training data then the only permissible shapes are linear combinations of the original training data, although some methods for generalising this have been presented in the literature (Heimann and Meinzer, 2009).”
50 Years of object recognition: Directions forward (2013, background): “However, superquadrics have been successfully applied to other domains where there is significantly less variability in the image features, such as the medical imaging domain and the segmentation of the cardiac ventricles [111, 12].”