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
Localizing landmark sets in head CTs using random forests and a heuristic search algorithm for registration initialization.
TL;DR: This work proposes a programming strategy that relies on the automated random forest-based localization of multiple landmarks used to estimate an initial transformation with a point-based registration method, and shows that it produces results that are equivalent to a manual initialization.
Journal ArticleDOI
Object recognition in medical images via anatomy-guided deep learning
Chao Jin,Jayaram K. Udupa,Liming Zhao,Yu Tong,Dewey Odhner,Gargi Pednekar,Sanghita Nag,Sharon Lewis,Nicholas Poole,Sutirth Mannikeri,S. Govindasamy,Aarushi Singh,Joseph Camaratta,Steve Owens,Drew A. Torigian +14 more
TL;DR: In this paper , an anatomy-guided deep learning object recognition approach named AAR-DL is proposed, which combines an advanced anatomy-modeling strategy, model-based non-deep-learning object recognition, and deep learning detection networks to achieve expert human-like performance.
Journal ArticleDOI
Body region localization in whole-body low-dose CT images of PET/CT scans using virtual landmarks.
TL;DR: A new solution to trim automatically the given axial image stack into image volumes satisfying the given body region definition using the concept of virtual landmarks, which achieves localization accuracy within 2-3 slices.
Proceedings ArticleDOI
Automatic Detection of the Nasal Cavities and Paranasal Sinuses Using Deep Neural Networks
TL;DR: An approach to individually detect all sinuses and the nasal cavity and the use of an irregular polyhedron for a better delimitation of their borders is proposed.
Book ChapterDOI
Handling of Feature Space Complexity for Texture Analysis in Medical Images
Yang Song,Weidong Cai +1 more
TL;DR: This chapter suggests that designing a classifier model that explicitly addresses the feature space complexity is an alternative direction in research, and provides a comprehensive review of such classification methods, including ensemble classification, subcategorization, and sparse representation.
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.