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• W3024457871 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 <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> = 0.834 (left hippocampus), and R <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> = 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." @default.
• W3024457871 created "2020-05-21" @default.
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• W3024457871 date "2020-01-01" @default.
• W3024457871 modified "2023-10-03" @default.
• W3024457871 title "Automatic Localization and Discrete Volume Measurements of Hippocampi From MRI Data Using a Convolutional Neural Network" @default.
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• W3024457871 doi "https://doi.org/10.1109/access.2020.2994388" @default.
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