3-D active appearance models: segmentation of cardiac MR and ultrasound images

By Steven C. Mitchell, J. Bosch, Boudewijn P. F. Lelieveldt, R. Geest, Johan Hans C. Reiber, Milan Sonka. IEEE Transactions on Medical Imaging, 2002

465 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/80698403816e2129ac9c7b6854cb1f1c99ecbac6

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Citations in the corpus

Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI (2008, methodology): “This model incorporates knowledge about cardiac structure and, thus, provides a robust methodology for performing the segmentation (Mitchell et al., 2001, 2002).”
Automatic Model-Based Segmentation of the Heart in CT Images (2008, methodology): “duced in [20], Stegmann and Pedersen [21] propose to compute the ejection fraction of the LV using a bitemporal model which couples the shape and appearance information from the end-diastole and end-systole cardiac phases.”
Machine Learning and Radiology (2012, background): “Applications of AAMs and ASMs in radiology include cardiac MR and ultrasound images (Mitchell et al., 2002), prostate segmentation (Shen et al., 2003), and segmenting thrombus in abdominal aortic aneurysms (de Bruijne et al., 2003).”
Statistical shape models for 3D medical image segmentation: A review (2009, background): “Nonetheless, complete region-based appearance models can be built for these cases by reducing the resolution of the employed texture, as several studies demonstrate (Beichel et al., 2002; Bosch et al., 2002; Mitchell et al., 2002).”
Hierarchical, model‐based merging of multiple fragments for improved three‐dimensional segmentation of nuclei (2005, background): “Examples of object modeling approaches include the shape model (36–38), the active appearance model, and the deformable model (11,39).”
Computational mesh as a descriptor of left ventricular shape for clinical diagnosis (2013, background): “Despite their successful application for the automatic segmentation of ventricular anatomy [5,6], cardiac atlases have, to date, made a limited clinical impact, with only a small number of studies using atlases to compare populations of subjects [7,8].”
Four-Chamber Heart Modeling and Automatic Segmentation for 3-D Cardiac CT Volumes Using Marginal Space Learning and Steerable Features (2008, unknown): “Compared with results on other imaging modalities [5], [22], to the best of our knowledge, our approach is also the fastest.”
Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? (2018, methodology): “The second group uses methods with strong prior including shape prior based deformable models [5], active shape and appearance models [25] and atlas based methods [26], all requiring a training dataset with manual annotations.”
A review of segmentation methods in short axis cardiac MR images (2011, methodology): “The extension to 3D AAM and ASM is presented in (Mitchell et al., 2002; Stegmann and Pedersen, 2005; van Assen et al., 2006), although not straightforward, especially because the model requires point correspondences between shapes (Figure 10).”
Optimal Surface Segmentation in Volumetric Images-A Graph-Theoretic Approach (2006, methodology): “Some others were previously tackled by using slice-by-slice or 3D model-based methods, including the detection of arterial wall and plaque from in vitro MR [68] and intravascular ultrasound (IVUS) images [6], [72], [73], segmentation of the endocardial and epicardial boundaries of the left ventricle”
Ultrasound image segmentation: a survey (2006, methodology): “Mitchell et al. [48] developed a 3-”
FAME-a flexible appearance modeling environment (2003, unknown): “[16], [17] and Lelieveldt et al.”