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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Journal ArticleDOI
BRR-Net: A tandem architectural CNN–RNN for automatic body region localization in CT images
TL;DR: A method to automatically demarcate the superior and inferior boundaries for neck, thorax, abdomen, and pelvis body regions in computed tomography (CT) images is described, which significantly outperforms earlier works by a large margin.
Proceedings ArticleDOI
Automatic anatomy partitioning of the torso region on CT images by using a deep convolutional network with majority voting
Xiangrong Zhou,Takuya Kojima,Song Wang,Xinxin Zhou,Takeshi Hara,Taiki Nozaki,Masaki Matsusako,Hiroshi Fujita +7 more
TL;DR: The experimental results demonstrated that using a deep CNN for anatomy partitioning on 3D CT images was more efficient and useful compared to the method used in the previous work.
Book ChapterDOI
Automatic Craniomaxillofacial Landmark Digitization via Segmentation-Guided Partially-Joint Regression Forest Model
TL;DR: A segmentation-guided partially-joint regression forest model is proposed that can automatically digitizes CMF landmarks and shows that the accuracy of automatically digitized landmarks using this approach is clinically acceptable.
Proceedings ArticleDOI
Implementation and Performance Optimization of Dynamic Random Forest
Xiaolong Xu,Wen Chen +1 more
TL;DR: This paper implements the DRF algorithm, and proposes a new weight update method, that is, giving higher weight to the samples classified wrongly by the current forest, giving lower weight to those samples classified correctly by theCurrent forest, so that the next tree will be more concerned with those misclassified samples.
Journal ArticleDOI
Quantitative normal thoracic anatomy at CT
TL;DR: The proposed method provides new, objective, and usable knowledge about anatomy whose utility in building body-wide models toward AAR has been demonstrated in other studies.
References
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Journal ArticleDOI
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.
Journal ArticleDOI
Classification and Regression Trees.
Book
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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.