Regression forests for efficient anatomy detection and localization in computed tomography scans
TL;DR: 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.
Citations
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TL;DR: The Ivy Glioblastoma Atlas is presented, an anatomically based transcriptional atlas of human gliOBlastoma that aligns individual histologic features with genomic alterations and gene expression patterns, thus assigning molecular information to the most important morphologic hallmarks of the tumor.
Abstract: Glioblastoma is an aggressive brain tumor that carries a poor prognosis. The tumor’s molecular and cellular landscapes are complex, and their relationships to histologic features routinely used for diagnosis are unclear. We present the Ivy Glioblastoma Atlas, an anatomically based transcriptional atlas of human glioblastoma that aligns individual histologic features with genomic alterations and gene expression patterns, thus assigning molecular information to the most important morphologic hallmarks of the tumor. The atlas and its clinical and genomic database are freely accessible online data resources that will serve as a valuable platform for future investigations of glioblastoma pathogenesis, diagnosis, and treatment.
349 citations
07 Oct 2012
TL;DR: It is demonstrated that Random Forest regression-voting can be used to generate high quality response images quickly and leads to fast and accurate shape model matching when applied in the Constrained Local Model framework.
Abstract: A widely used approach for locating points on deformable objects is to generate feature response images for each point, then to fit a shape model to the response images. We demonstrate that Random Forest regression can be used to generate high quality response images quickly. Rather than using a generative or a discriminative model to evaluate each pixel, a regressor is used to cast votes for the optimal position. We show this leads to fast and accurate matching when combined with a statistical shape model. We evaluate the technique in detail, and compare with a range of commonly used alternatives on several different datasets. We show that the random forest regression method is significantly faster and more accurate than equivalent discriminative, or boosted regression based methods trained on the same data.
343 citations
Cites background or methods from "Regression forests for efficient an..."
...Regression based voting: Since the introduction of the Generalised Hough Transform [19] voting based methods have been shown to be effective for locating shapes in images, and there have been many variants....
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...For instance, this could be the mean, d̄, and covariance Sd of these samples, or the full set of samples....
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...Our method differs from this in that we use an explicit shape model to find the best combination of points....
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TL;DR: This work localizes the pancreas from the entire 3D CT scan, providing a reliable bounding box for the more refined segmentation step, and introduces a fully deep‐learning approach, based on an efficient application of holistically‐nested convolutional networks (HNNs) on the three orthogonal axial, sagittal, and coronal views.
290 citations
Cites background or methods from "Regression forests for efficient an..."
...7, the conventional organ localization framework using regression forest [43], [44] does not address well the purpose of candidate region generation for segmentation where extremely high pixel-to-pixel recall is required since it is mainly designed for organ detection....
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...Aggregation can be done with non-maximum suppression on prediction voting maps, mean aggregation [43], cluster medoid aggregation [45], and the use of local appearance with discriminative models to accept or reject predictions [44]....
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...1) Regression Forest: Object localization by regression has been studied extensively in the literature including [43], [45], [44]....
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...Conventional organ localization methods using random forest regression [43], [44], which we explain in Sec....
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TL;DR: This work introduces a framework for supervised segmentation based on multiple modality intensity, geometry, and asymmetry feature sets that interface the supervised learning capabilities of the random forest model with regularized probabilistic segmentation using the recently developed ANTsR package.
Abstract: Segmenting and quantifying gliomas from MRI is an important task for diagnosis, planning intervention, and for tracking tumor changes over time. However, this task is complicated by the lack of prior knowledge concerning tumor location, spatial extent, shape, possible displacement of normal tissue, and intensity signature. To accommodate such complications, we introduce a framework for supervised segmentation based on multiple modality intensity, geometry, and asymmetry feature sets. These features drive a supervised whole-brain and tumor segmentation approach based on random forest-derived probabilities. The asymmetry-related features (based on optimal symmetric multimodal templates) demonstrate excellent discriminative properties within this framework. We also gain performance by generating probability maps from random forest models and using these maps for a refining Markov random field regularized probabilistic segmentation. This strategy allows us to interface the supervised learning capabilities of the random forest model with regularized probabilistic segmentation using the recently developed ANTsR package--a comprehensive statistical and visualization interface between the popular Advanced Normalization Tools (ANTs) and the R statistical project. The reported algorithmic framework was the top-performing entry in the MICCAI 2013 Multimodal Brain Tumor Segmentation challenge. The challenge data were widely varying consisting of both high-grade and low-grade glioma tumor four-modality MRI from five different institutions. Average Dice overlap measures for the final algorithmic assessment were 0.87, 0.78, and 0.74 for "complete", "core", and "enhanced" tumor components, respectively.
245 citations
Cites background from "Regression forests for efficient an..."
...2013), anatomy detection in computed tomography (Criminisi et al. 2013), and segmentation of echocardiographic images (Verhoek et al....
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...…brain tumor segmentation (Bauer et al. 2012; Zikic et al. 2012), brain extraction (Iglesias et al. 2010), classification of Alzheimer’s disease (Gray et al. 2013), anatomy detection in computed tomography (Criminisi et al. 2013), and segmentation of echocardiographic images (Verhoek et al. 2011)....
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TL;DR: It is demonstrated that Random Forest regression-voting can be used to generate high quality response images quickly and leads to fast and accurate shape model matching when applied in the Constrained Local Model framework.
Abstract: A widely used approach for locating points on deformable objects in images is to generate feature response images for each point, and then to fit a shape model to these response images. We demonstrate that Random Forest regression-voting can be used to generate high quality response images quickly. Rather than using a generative or a discriminative model to evaluate each pixel, a regressor is used to cast votes for the optimal position of each point. We show that this leads to fast and accurate shape model matching when applied in the Constrained Local Model framework. We evaluate the technique in detail, and compare it with a range of commonly used alternatives across application areas: the annotation of the joints of the hands in radiographs and the detection of feature points in facial images. We show that our approach outperforms alternative techniques, achieving what we believe to be the most accurate results yet published for hand joint annotation and state-of-the-art performance for facial feature point detection.
241 citations
Cites background or methods from "Regression forests for efficient an..."
...RFs have been shown to be effective in a wide range of classification and regression problems [16], [17], [18]....
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...In related work, [17] and [16] produce votes in higher dimensional spaces (3D or 6D), but work directly with the vector votes rather than accumulator arrays....
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...[16] used RF regression to vote for the positions of the sides of bounding boxes around organs in CT images....
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References
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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.
Abstract: 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. The simplex adapts itself to the local landscape, and contracts on to the final minimum. The method is shown to be effective and computationally compact. A procedure is given for the estimation of the Hessian matrix in the neighbourhood of the minimum, needed in statistical estimation problems.
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"Regression forests for efficient an..." refers methods in this paper
...The second algorithm (‘Simplex’) is our own implementation and works by maximizing correlation-coefficient between the aligned images using the simplex method as the optimizer (Nelder and Mead, 1965)....
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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.
Abstract: The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
14,825 citations
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.
Abstract: Much of previous attention on decision trees focuses on the splitting criteria and optimization of tree sizes. The dilemma between overfitting and achieving maximum accuracy is seldom resolved. 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. The classifier consists of multiple trees constructed systematically by pseudorandomly selecting subsets of components of the feature vector, that is, trees constructed in randomly chosen subspaces. The subspace method is compared to single-tree classifiers and other forest construction methods by experiments on publicly available datasets, where the method's superiority is demonstrated. We also discuss independence between trees in a forest and relate that to the combined classification accuracy.
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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.
Abstract: In this paper the authors present a new approach for the nonrigid registration of contrast-enhanced breast MRI. A hierarchical transformation model of the motion of the breast has been developed. The global motion of the breast is modeled by an affine transformation while the local breast motion is described by a free-form deformation (FFD) based on B-splines. Normalized mutual information is used as a voxel-based similarity measure which is insensitive to intensity changes as a result of the contrast enhancement. Registration is achieved by minimizing a cost function, which represents a combination of the cost associated with the smoothness of the transformation and the cost associated with the image similarity. The algorithm has been applied to the fully automated registration of three-dimensional (3-D) breast MRI in volunteers and patients. In particular, the authors have compared the results of the proposed nonrigid registration algorithm to those obtained using rigid and affine registration techniques. The results clearly indicate that the nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms.
5,490 citations