Automatic labeling of MR brain images through extensible learning and atlas forests

Purpose: Multiatlas‐based method is extensively used in MR brain images segmentation because of its simplicity and robustness. This method provides excellent accuracy although it is time consuming and limited in terms of obtaining information about new atlases. In this study, an automatic labeling of MR brain images through extensible learning and atlas forest is presented to address these limitations. Methods: We propose an extensible learning model which allows the multiatlas‐based framework capable of managing the datasets with numerous atlases or dynamic atlas datasets and simultaneously ensure the accuracy of automatic labeling. Two new strategies are used to reduce the time and space complexity and improve the efficiency of the automatic labeling of brain MR images. First, atlases are encoded to atlas forests through random forest technology to reduce the time consumed for cross‐registration between atlases and target image, and a scatter spatial vector is designed to eliminate errors caused by inaccurate registration. Second, an atlas selection method based on the extensible learning model is used to select atlases for target image without traversing the entire dataset and then obtain the accurate labeling. Results: The labeling results of the proposed method were evaluated in three public datasets, namely, IBSR, LONI LPBA40, and ADNI. With the proposed method, the dice coefficient metric values on the three datasets were 84.17 ± 4.61%, 83.25 ± 4.29%, and 81.88 ± 4.53% which were 5% higher than those of the conventional method, respectively. The efficiency of the extensible learning model was evaluated by state‐of‐the‐art methods for labeling of MR brain images. Experimental results showed that the proposed method could achieve accurate labeling for MR brain images without traversing the entire datasets. Conclusion: In the proposed multiatlas‐based method, extensible learning and atlas forests were applied to control the automatic labeling of brain anatomies on large atlas datasets or dynamic atlas datasets and obtain accurate results.

[1]  Yaozong Gao,et al.  Concatenated spatially-localized random forests for hippocampus labeling in adult and infant MR brain images , 2017, Neurocomputing.

[2]  Ben Glocker,et al.  Decision Forests for Tissue-Specific Segmentation of High-Grade Gliomas in Multi-channel MR , 2012, MICCAI.

[3]  Carlos Ortiz-de-Solorzano,et al.  Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data , 2009, IEEE Transactions on Medical Imaging.

[4]  Antonio Criminisi,et al.  Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning , 2012, Found. Trends Comput. Graph. Vis..

[5]  Mario Giacobini,et al.  Automatic segmentation of hippocampus in histological images of mouse brains using deformable models and random forest , 2012, 2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS).

[6]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[7]  Xuelong Li,et al.  Segmenting Images by Combining Selected Atlases on Manifold , 2011, MICCAI.

[8]  Max A. Viergever,et al.  Label Fusion in Atlas-Based Segmentation Using a Selective and Iterative Method for Performance Level Estimation (SIMPLE) , 2010, IEEE Transactions on Medical Imaging.

[9]  Ben Glocker,et al.  Atlas Encoding by Randomized Forests for Efficient Label Propagation , 2013, MICCAI.

[10]  Manuel Graña,et al.  Random forest active learning for AAA thrombus segmentation in computed tomography angiography images , 2014, Neurocomputing.

[11]  Ben Glocker,et al.  Encoding atlases by randomized classification forests for efficient multi-atlas label propagation , 2014, Medical Image Anal..

[12]  B. A. Shepherd,et al.  An Appraisal of a Decision Tree Approach to Image Classification , 1983, IJCAI.

[13]  Josien P W Pluim,et al.  Multiatlas-based segmentation with preregistration atlas selection. , 2013, Medical physics.

[14]  Torsten Rohlfing,et al.  Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains , 2004, NeuroImage.

[15]  Nassir Navab,et al.  Learning from Multiple Experts with Random Forests: Application to the Segmentation of the Midbrain in 3D Ultrasound , 2013, MICCAI.

[16]  Mert R. Sabuncu,et al.  A Generative Model for Image Segmentation Based on Label Fusion , 2010, IEEE Transactions on Medical Imaging.

[17]  Daniel Rueckert,et al.  Multi-atlas segmentation with augmented features for cardiac MR images , 2015, Medical Image Anal..

[18]  Tianzi Jiang,et al.  Local label learning (LLL) for subcortical structure segmentation: Application to hippocampus segmentation , 2014, Human brain mapping.

[19]  Daniel Rueckert,et al.  Automatic anatomical brain MRI segmentation combining label propagation and decision fusion , 2006, NeuroImage.

[20]  Tom Vercauteren,et al.  Diffeomorphic demons: Efficient non-parametric image registration , 2009, NeuroImage.

[21]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[22]  Daoqiang Zhang,et al.  A generative probability model of joint label fusion for multi-atlas based brain segmentation , 2014, Medical Image Anal..

[23]  Sabina Sonia Tangaro,et al.  Random Forest Classification for Hippocampal Segmentation in 3D MR Images , 2013, 2013 12th International Conference on Machine Learning and Applications.

[24]  Max A. Viergever,et al.  elastix: A Toolbox for Intensity-Based Medical Image Registration , 2010, IEEE Transactions on Medical Imaging.

[25]  Dinggang Shen,et al.  Iterative multi-atlas-based multi-image segmentation with tree-based registration , 2012, NeuroImage.

[26]  Arno Klein,et al.  101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol , 2012, Front. Neurosci..

[27]  Bennett A. Landman,et al.  Non-local STAPLE: An Intensity-Driven Multi-atlas Rater Model , 2012, MICCAI.

[28]  Sébastien Ourselin,et al.  Using Manifold Learning for Atlas Selection in Multi-Atlas Segmentation , 2013, PloS one.

[29]  Max A. Viergever,et al.  Multi-Atlas-Based Segmentation With Local Decision Fusion—Application to Cardiac and Aortic Segmentation in CT Scans , 2009, IEEE Transactions on Medical Imaging.

[30]  Daniel Rueckert,et al.  Spatially Aware Patch-Based Segmentation (SAPS): An Alternative Patch-Based Segmentation Framework , 2012, MCV.

[31]  Alain Trouvé,et al.  Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms , 2005, International Journal of Computer Vision.

[32]  Colin Studholme,et al.  A Supervised Patch-Based Approach for Human Brain Labeling , 2011, IEEE Transactions on Medical Imaging.

[33]  Yaozong Gao,et al.  Automatic labeling of MR brain images by hierarchical learning of atlas forests. , 2016, Medical physics.