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

Abstract

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.

Automatic Localization and Discrete Volume Measurements of Hippocampi From MRI Data Using a Convolutional Neural Network

“…Prior to measuring the number of voxels from each slice, it is necessary to locate the left and right hippocampi inside an MRI scan. The locations of the left and right hippocampi were estimated using a two-stage Hough-CNN model similar to that in [29]. Using these estimated locations, 3-D patches were extracted, and then the slices were separated into 2-D patches for further preprocessing.…”

“…[43] [29] are used to detect and localize objects from images of different modalities. Also, a two phase, multimodel automatic brain tumour diagnosis system was developed using CNN in [44].…”

“…The CNN models were designed in a similar way as those in [29], [50] to localize the left and right hippocampi. The network architecture has been changed slightly for this research work.…”

“…The two-stage ensemble Hough CNN [29] was implemented with a minor change. The network sizes were kept the same for the global Hough CNN (GH-CNN) and the local Hough CNN (LH-CNN).…”

“…We explored a deep learning algorithm to analyze MRI scans to localize the hippocampi and measure the discrete volumes. The hippocampi were localized automatically using two stage ensemble Hough convolutional neural network (Hough-CNN) model [29], and the voxel positions of hippocampi were traced. Using those voxel locations, 2-D patches of the left and right hippocampi were extracted to train deep learning models.…”