Construction of an abdominal probabilistic atlas and its application in segmentation

By Hyunjin Park, Peyton H. Bland, Charles R. Meyer. IEEE Transactions on Medical Imaging, 2003

375 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/272c685823a8df4fa075a6a240983571114f8a3d

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Citations in the corpus

The Liver Tumor Segmentation Benchmark (LiTS) (2019, methodology): “proposed the first PA utilizing 32 abdominal CT series for registration based on mutual information and thin plate splines as warping transformations[32] and a Markov random field (MRF)[32] for segmentation.”
Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation (2022, background): “indicates the probability of the object appearing at some location, is widely applied in medical image segmentation [108], [115], [116], [117].”
Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data (2009, methodology): “Information from several reference images can be combined into an average atlas [12], [13] or, if probability values for each particular location are included, into a so-called probabilistic atlas [ 1 ], [2], [4], [9].”
Toward automated segmentation of the pathological lung in CT (2005, methodology): “Atlas-based segmentation has been used on the normal and the pathological brain [15], [16] and the heart [17], and recently also on organs in the abdomen [ 18 ] and lungs [19], [20].”
Non-rigid image registration: theory and practice. (2004, methodology): “An ambitious attempt to construct a CT atlas of the liver, kidneys and spinal cord is described in Park et al [125] where thin-plate splines and mutual information were used to register 31 CT images to a reference.”
Reducing the Hausdorff Distance in Medical Image Segmentation With Convolutional Neural Networks (2019, methodology): “Atlas-based segmentation methods, which are another class of widely-used techniques, work by registering a set of reference images to the target image by minimizing such global loss functions as the sum of squared difference of image intensities or the mutual information [12], [13].”
Multi-Atlas Segmentation of Biomedical Images: A Survey (2014, background): “However, the approach that dominated early atlas-guided segmentation was probabilistic atlas-based segmentation (Ashburner and Friston, 2005; Fischl et al., 2002; Park et al., 2003; Pohl et al., 2006; Thomas Yeo et al., 2008), which had two distinctive properties.”
Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets (2009, methodology): “Methods that have been examined for this task include deformable models [10], a probabilistic atlas [11], and statistical shape models [12], [13].”