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

Abstract

Automatic hippocampal volume measurement from brain magnetic resonance imaging (MRI) is a crucial task and an important research area, especially in the study of neurodegenerative diseases; hippocampal volume atrophy is known to be connected with Alzheimer's disease. In this research work, we propose a deep learning-based method to automatically measure the discrete hippocampal volume without prior segmentation of the volumetric MRI scans. We constructed a 2-D convolutional neural network (CNN) model that uses 3-channel 2-D patches to predict the number of voxels attributed to the hippocampus; the number of estimated hippocampal voxels is multiplied by the voxel volume to measure the discrete volume of the hippocampus. In addition, we demonstrate a preprocessing scheme to prepare the data using a relatively small number of MRI scans. The average errors in the measured volumes of the proposed approach and the compared atlas-based system were 4.3173 ± 3.5436 (avg. error% ± STD) and 4.1562 ±3.5262 (avg. error % ± STD) for the left and right hippocampi, respectively. The correlation coefficients of the proposed approach with atlas-based volume measurement were statistically significant (p-value < 0.01, R 2 = 0.834 (left hippocampus), and R 2 = 0.848 (right hippocampus) based on 0.05 significance level), which suggests that the proposed approach can be used as a proxy method for the atlas-based system. Furthermore, the proposed approach is computationally efficient and requires less than 2 seconds to calculate the number of voxels for an MRI scan. Moreover, our method outperforms the state-of-the-art deep learning approach, such as 2-D U-Net and SegNet in the context of voxel/volume estimation errors% for the left and right hippocampi.

Accurate Volume Calculation Driven by Delaunay Triangulation for Coal Measurement

“…In [35], a slice is called a voxel, which represents a particular space that has been divided into a grid of identically sized and equally spaced cubes. Basher et al [6] split the hippocampus into voxels. A convolutional neural network (CNN) model is designed to predict the number of voxels attributed to the hippocampus, and the number of estimated hippocampal voxels is multiplied by the voxel volume to measure the discrete volume of the hippocampus.…”

“…Volume calculation is to compute the space occupied by an object through point cloud that generated from 3D reconstruction. Volume calculation underpins many crucial applications, such as the volume measurement of coal [1], ore [2], earthwork [3], and tree [4,5] and the disease diagnosis in medical field [6,7]. is paper aims to the coal measurement, where volume calculation has been a challenging task due to the difficulty in determining the object boundary from the scatters of point cloud.…”

Multi-Layer Picture of Neurodegenerative Diseases: Lessons from the Use of Big Data through Artificial Intelligence

“…CNNs can also be applied in the segmentation task to quantify structural changes in brain shape, volume, and thickness that may be related to neurodegeneration [18,27]. As the assessments of the brainstem and hippocampal volumes are known to be crucial tasks in the study of ND, a 2D CNN was recently used to predict the number of voxels attributed to the hippocampus [28]. Meanwhile, an automated sub-cortical brain structure segmentation approach based on a CNN architecture outperformed state-of-the-art techniques such as Free Surfer on the Internet Brain Segmentation Repository (IBSR 18) dataset [29].…”

Detection of bulk feed volume based on binocular stereo vision

“…The algorithm is also prone to holes. Volume measurement by point cloud has a vital application basis in the fields of coal 6 , trees 7 , 8 , and hospital disease diagnosis 9 , 10 .…”