Hippocampus Localization Using a Two-Stage Ensemble Hough Convolutional Neural Network

In this paper, we present a two-stage ensemble-based approach to localize the anatomical structure of interest from magnetic resonance imaging (MRI) scans. We combine a Hough voting method with a convolutional neural network to automatically localize brain anatomical structures such as the hippocampus. The hippocampus is one of the regions that can be affected by the Alzheimer’s disease, and this region is known to be related to memory loss. The structural changes of the hippocampus are important biomarkers for dementia. To analyze the structural changes, accurate localization plays a vital role. Furthermore, for segmentation and registration of anatomical structures, exact localization is desired. Our proposed models use a deep convolutional neural network (CNN) to calculate displacement vectors by exploiting the Hough voting strategy from multiple 3-viewpoint patch samples. The displacement vectors are added to the sample position to estimate the target position. To efficiently learn from samples, we employed a local and global strategy. The multiple global models were trained using randomly selected 3-viewpoint patches from the whole MRI scan. The results from global models are aggregated to obtain global predictions. Similarly, we trained multiple local models, extracting patches from the vicinity of the hippocampus location and assembling them to obtain a local prediction. The proposed models exploit the Alzheimer’s disease neuroimaging initiative (ADNI) MRI dataset and the Gwangju Alzheimer’s and related dementia (GARD) cohort MRI dataset for training, validating and testing. The average prediction error using the proposed two-stage ensemble Hough convolutional neural network (Hough-CNN) models are 2.32 and 2.25 mm for the left and right hippocampi, respectively, for 65 test MRIs from the GARD cohort dataset. Similarly, for the ADNI MRI dataset, the average prediction error for the left and right hippocampi are 2.31 and 2.04 mm, respectively, for 56 MRI scans.

[1]  Isabelle Bloch,et al.  Integration of fuzzy spatial relations in deformable models - Application to brain MRI segmentation , 2006, Pattern Recognit..

[2]  D. Louis Collins,et al.  Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation , 2011, NeuroImage.

[3]  Stephen M. Smith,et al.  A Bayesian model of shape and appearance for subcortical brain segmentation , 2011, NeuroImage.

[4]  Luc Van Gool,et al.  A Hough transform-based voting framework for action recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Yasuhiro Kawasaki,et al.  Male-specific volume expansion of the human hippocampus during adolescence. , 2004, Cerebral cortex.

[6]  Taghi M. Khoshgoftaar,et al.  Selecting the Appropriate Ensemble Learning Approach for Balanced Bioinformatics Data , 2015, FLAIRS.

[7]  Antoine Manzanera,et al.  Fast growing hough forest as a stable model for object detection , 2016, 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA).

[8]  Isabelle Bloch,et al.  Combining Radiometric and Spatial Structural Information in a New Metric for Minimal Surface Segmentation , 2007, IPMI.

[9]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[10]  N Sriraam,et al.  Automated epileptic seizures detection using multi-features and multilayer perceptron neural network , 2018, Brain Informatics.

[11]  H. Benali,et al.  Fully automatic hippocampus segmentation and classification in Alzheimer's disease and mild cognitive impairment applied on data from ADNI , 2009, Hippocampus.

[12]  Hans-Peter Meinzer,et al.  Statistical shape models for 3D medical image segmentation: A review , 2009, Medical Image Anal..

[13]  Seyed-Ahmad Ahmadi,et al.  Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound , 2016, Comput. Vis. Image Underst..

[14]  Özkan Kiliç,et al.  Classification of lung sounds using convolutional neural networks , 2017, EURASIP Journal on Image and Video Processing.

[15]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[16]  Giovanni Montana,et al.  Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks , 2015, ICPRAM 2015.

[17]  Mandava Rajeswari,et al.  Hippocampus Localization Guided by Coherent Point Drift Registration Using Assembled Point Set , 2013, HAIS.

[18]  Dorin Comaniciu,et al.  Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Hale Kim,et al.  Full weighting Hough Forests for object detection , 2014, 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[20]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[21]  Ebroul Izquierdo,et al.  Rethinking random Hough Forests for video database indexing and pattern search , 2016, Computational Visual Media.

[22]  Maryam Hajiesmaeili,et al.  A new approach to locate the hippocampus nest in brain MR images , 2017, 2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA).

[23]  Bernard Zenko,et al.  Clusters of male and female Alzheimer’s disease patients in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database , 2016, Brain Informatics.

[24]  Pushmeet Kohli,et al.  On Detection of Multiple Object Instances Using Hough Transforms , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  K. Somasundaram,et al.  The extraction of hippocampus from MRI of human brain using morphological and image binarization techniques , 2014, 2014 International Conference on Electronics and Communication Systems (ICECS).

[26]  Arthur W. Toga,et al.  Construction of a 3D probabilistic atlas of human cortical structures , 2008, NeuroImage.

[27]  Horst Bischof,et al.  Hough-based tracking of non-rigid objects , 2011, 2011 International Conference on Computer Vision.

[28]  Jyoti Islam,et al.  Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks , 2018, Brain Informatics.

[29]  Dan Zhang,et al.  Segmentation of Hippocampus in MRI Images Based on the Improved Level Set , 2011, 2011 Fourth International Symposium on Computational Intelligence and Design.

[30]  Luc Van Gool,et al.  Hough Forests for Object Detection, Tracking, and Action Recognition , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Guido Gerig,et al.  User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability , 2006, NeuroImage.

[32]  Meritxell Bach Cuadra,et al.  A review of atlas-based segmentation for magnetic resonance brain images , 2011, Comput. Methods Programs Biomed..

[33]  Dana H. Ballard,et al.  Generalizing the Hough transform to detect arbitrary shapes , 1981, Pattern Recognit..

[34]  Hesham F. A. Hamed,et al.  Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks , 2018, EURASIP Journal on Image and Video Processing.

[35]  Cristian Lorenz,et al.  Discriminative generalized Hough transform for object localization in medical images , 2013, International Journal of Computer Assisted Radiology and Surgery.

[36]  Walid Abdullah Al,et al.  Automatic aortic valve landmark localization in coronary CT angiography using colonial walk , 2018, PloS one.

[37]  Hamid Soltanian-Zadeh,et al.  Knowledge-based localization of hippocampus in human brain MRI , 1999, Medical Imaging.

[38]  Tae-Kyun Kim,et al.  Latent-Class Hough Forests for 6 DoF Object Pose Estimation , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[39]  Shuqian Luo,et al.  The Application of Watersnakes Algorithm in Segmentation of the Hippocampus from Brain MR Image , 2007, MIMI.

[40]  Horst Bischof,et al.  Hough Networks for Head Pose Estimation and Facial Feature Localization , 2014, BMVC.

[41]  N. Varuna Shree,et al.  Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network , 2018, Brain Informatics.