Journal ArticleDOI
Regression forests for efficient anatomy detection and localization in computed tomography scans
Antonio Criminisi,Duncan Robertson,Ender Konukoglu,Jamie Shotton,Sayan Pathak,Steve J. White,Khan M. Siddiqui +6 more
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TLDR
A new, continuous parametrization of the anatomy localization problem, which allows it to be addressed effectively by multi-class random regression forests, and is more accurate and robust than techniques based on efficient multi-atlas registration and template-based nearest-neighbor detection.About:
This article is published in Medical Image Analysis.The article was published on 2013-12-01. It has received 251 citations till now. The article focuses on the topics: Random forest.read more
Citations
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Knowledge-Aided Convolutional Neural Network for Small Organ Segmentation
Yu Zhao,Hongwei Li,Shaohua Wan,Anjany Sekuboyina,Xiaobin Hu,Giles Tetteh,Marie Piraud,Bjoern H. Menze +7 more
TL;DR: Experimental results demonstrate that the proposed method outperforms cutting-edge deep learning approaches, traditional forest-based approaches, and multi-atlas approaches in the segmentation of small organs.
Journal ArticleDOI
Detecting Anatomical Landmarks for Fast Alzheimer’s Disease Diagnosis
TL;DR: The landmark-based feature extraction method is proposed that is comparable to, or even better than, that achieved by existing region-based and voxel-based methods, while the AD classification performance of the method is approximately 50 times faster.
Journal ArticleDOI
Abdominal multi-organ segmentation from CT images using conditional shape–location and unsupervised intensity priors
Toshiyuki Okada,Marius George Linguraru,Masatoshi Hori,Ronald M. Summers,Noriyuki Tomiyama,Yoshinobu Sato +5 more
TL;DR: A general framework of multi-organ segmentation which effectively incorporates interrelations among multiple organs and easily adapts to various imaging conditions without the need for supervised intensity information is proposed.
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Evaluation of Six Registration Methods for the Human Abdomen on Clinically Acquired CT
Zhoubing Xu,Christopher P. Lee,Mattias P. Heinrich,Marc Modat,Daniel Rueckert,Sebastien Ourselin,Richard G. Abramson,Bennett A. Landman +7 more
TL;DR: There is substantial room for improvement in image registration for abdominal CT, due to the overall low DSC values, and substantial portion of low-performing outliers.
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A Bottom-Up Approach for Pancreas Segmentation Using Cascaded Superpixels and (Deep) Image Patch Labeling
TL;DR: Under six-fold cross-validation, the bottom-up segmentation method significantly outperforms its MALF counterpart and the segmentation framework using deep patch labeling confidences is also more numerically stable, as reflected in the smaller performance metric standard deviations.
References
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A simplex method for function minimization
John A. Nelder,R. Mead +1 more
TL;DR: A method is described for the minimization of a function of n variables, which depends on the comparison of function values at the (n 41) vertices of a general simplex, followed by the replacement of the vertex with the highest value by another point.
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Classification and Regression Trees.
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Classification and regression trees
TL;DR: The methodology used to construct tree structured rules is the focus of a monograph as mentioned in this paper, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
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The random subspace method for constructing decision forests
TL;DR: A method to construct a decision tree based classifier is proposed that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity.
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Nonrigid registration using free-form deformations: application to breast MR images
TL;DR: The results clearly indicate that the proposed nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms.