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
Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning.
Tan H. Nguyen,Shamira Sridharan,Virgilia Macias,Andre Kajdacsy-Balla,Jonathan Melamed,Minh N. Do,Gabriel Popescu +6 more
TL;DR: This approach will provide a clinically objective and quantitative metric for Gleason grading, allowing us to corroborate results across instruments and laboratories and feed the computer algorithms for improved accuracy.
Journal ArticleDOI
Efficient Multiple Organ Localization in CT Image Using 3D Region Proposal Network
TL;DR: An efficient method for multiple organ localization in CT image using a 3D region proposal network that achieves higher detection precision and localization accuracy than the current state-of-the-art methods with approximate 4 to 18 times faster processing speed is proposed.
Proceedings ArticleDOI
2D image classification for 3D anatomy localization: employing deep convolutional neural networks
TL;DR: The results demonstrate that accurate automatic 3D localization of anatomical structures by CNN-based 2D image classification is feasible.
Journal ArticleDOI
Convolutional networks for kidney segmentation in contrast-enhanced CT scans
TL;DR: A fully automatic framework for kidney segmentation with convolutional networks (ConvNets) in contrast-enhanced computerised tomography (CT) scans using a patch-wise approach to predict the class membership of the central voxel in 2D patches is presented.
Journal ArticleDOI
Accurate Segmentation of CT Male Pelvic Organs via Regression-Based Deformable Models and Multi-Task Random Forests
TL;DR: This paper proposes a novel approach to guide deformable models, thus making them robust against arbitrary initializations, and learns a displacement regressor, which predicts 3D displacement from any image voxel to the target organ boundary based on the local patch appearance.
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.
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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.
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.