Combined Atlas and Convolutional Neural Network-Based Segmentation of the Hippocampus from MRI According to the ADNI Harmonized Protocol
Autor: | Philip A. Barber, Nils D. Forkert, Sandra E. Black, Sean M. Nestor, Samaneh Nobakht, Morgan J Schaeffer |
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Rok vydání: | 2020 |
Předmět: |
Computer science
convolutional neural network lcsh:Chemical technology Biochemistry Convolutional neural network Hippocampus Article 030218 nuclear medicine & medical imaging Analytical Chemistry 03 medical and health sciences 0302 clinical medicine Robustness (computer science) Alzheimer Disease medicine Image Processing Computer-Assisted Hippocampus (mythology) Humans lcsh:TP1-1185 Segmentation Electrical and Electronic Engineering Instrumentation HARP Protocol (science) medicine.diagnostic_test business.industry segmentation Pattern recognition Magnetic resonance imaging Magnetic Resonance Imaging Atomic and Molecular Physics and Optics Feature (computer vision) Artificial intelligence Neural Networks Computer business ADNI harmonized hippocampal protocol 030217 neurology & neurosurgery |
Zdroj: | Sensors (Basel, Switzerland) Sensors, Vol 21, Iss 2427, p 2427 (2021) Sensors Volume 21 Issue 7 |
ISSN: | 1424-8220 |
Popis: | Hippocampus atrophy is an early structural feature that can be measured from magnetic resonance imaging (MRI) to improve the diagnosis of neurological diseases. An accurate and robust standardized hippocampus segmentation method is required for reliable atrophy assessment. The aim of this work was to develop and evaluate an automatic segmentation tool (DeepHarp) for hippocampus delineation according to the ADNI harmonized hippocampal protocol (HarP). DeepHarp utilizes a two-step process. First, the approximate location of the hippocampus is identified in T1-weighted MRI datasets using an atlas-based approach, which is used to crop the images to a region-of-interest (ROI) containing the hippocampus. In the second step, a convolutional neural network trained using datasets with corresponding manual hippocampus annotations is used to segment the hippocampus from the cropped ROI. The proposed method was developed and validated using 107 datasets with manually segmented hippocampi according to the ADNI-HarP standard as well as 114 multi-center datasets of patients with Alzheimer’s disease, mild cognitive impairment, cerebrovascular disease, and healthy controls. Twenty-three independent datasets manually segmented according to the ADNI-HarP protocol were used for testing to assess the accuracy, while an independent test-retest dataset was used to assess precision. The proposed DeepHarp method achieved a mean Dice similarity score of 0.88, which was significantly better than four other established hippocampus segmentation methods used for comparison. At the same time, the proposed method also achieved a high test-retest precision (mean Dice score: 0.95). In conclusion, DeepHarp can automatically segment the hippocampus from T1-weighted MRI datasets according to the ADNI-HarP protocol with high accuracy and robustness, which can aid atrophy measurements in a variety of pathologies. |
Databáze: | OpenAIRE |
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