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Cerebral Microbleed Automatic Detection System Based on the “Deep Learning”

OBJECTIVE: To validate the reliability and efficiency of clinical diagnosis in practice based on a well-established system for the automatic segmentation of cerebral microbleeds (CMBs). METHOD: This is a retrospective study based on Magnetic Resonance Imaging-Susceptibility Weighted Imaging (MRI-SWI...

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Autores principales: Fan, Pingping, Shan, Wei, Yang, Huajun, Zheng, Yu, Wu, Zhenzhou, Chan, Shang Wei, Wang, Qun, Gao, Peiyi, Liu, Yaou, He, Kunlun, Sui, Binbin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8988858/
https://www.ncbi.nlm.nih.gov/pubmed/35402427
http://dx.doi.org/10.3389/fmed.2022.807443
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author Fan, Pingping
Shan, Wei
Yang, Huajun
Zheng, Yu
Wu, Zhenzhou
Chan, Shang Wei
Wang, Qun
Gao, Peiyi
Liu, Yaou
He, Kunlun
Sui, Binbin
author_facet Fan, Pingping
Shan, Wei
Yang, Huajun
Zheng, Yu
Wu, Zhenzhou
Chan, Shang Wei
Wang, Qun
Gao, Peiyi
Liu, Yaou
He, Kunlun
Sui, Binbin
author_sort Fan, Pingping
collection PubMed
description OBJECTIVE: To validate the reliability and efficiency of clinical diagnosis in practice based on a well-established system for the automatic segmentation of cerebral microbleeds (CMBs). METHOD: This is a retrospective study based on Magnetic Resonance Imaging-Susceptibility Weighted Imaging (MRI-SWI) datasets from 1,615 patients (median age, 56 years; 1,115 males, 500 females) obtained between September 2018 and September 2019. All patients had been diagnosed with cerebral small vessel disease (CSVD) with clear cerebral microbleeds (CMBs) on MRI-SWI. The patients were divided into training and validation cohorts of 1,285 and 330 patients, respectively, and another 30 patients were used for internal testing. The model training and validation data were labeled layer by layer and rechecked by two neuroradiologists with 15 years of work experience. Afterward, a three-dimensional convolutional neural network (CNN) was applied to the MRI data from the training and validation cohorts to construct a deep learning system (DLS) that was tested with the 72 patients, independent of the aforementioned MRI cohort. The DLS tool was used as a segmentation program for these 72 patients. These results were evaluated and revised by five neuroradiologists and subjected to an output analysis divided into the missed label, incorrect label, and correct label. The interneuroradiologists DLS agreement rate, which was assessed using the interrater agreement kappas test, was used for the quality analysis. RESULTS: In the detection and segmentation of the CMBs, the DLS achieved a Dice coefficient of 0.72. In the evaluation of the independent clinical data, the neuroradiologists reported that more than 90% of the lesions were directly detected and less than 10% of lesions were incorrectly labeled or the label was missed by our DLS. The kappa value for interneuroradiologist DLS agreement reached 0.79 on average. CONCLUSION: Based on the results, the automatic detection and segmentation of CMBs are feasible. The proposed well-trained DLS system might represent a trusted tool for the segmentation and detection of CMB lesions.
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spelling pubmed-89888582022-04-08 Cerebral Microbleed Automatic Detection System Based on the “Deep Learning” Fan, Pingping Shan, Wei Yang, Huajun Zheng, Yu Wu, Zhenzhou Chan, Shang Wei Wang, Qun Gao, Peiyi Liu, Yaou He, Kunlun Sui, Binbin Front Med (Lausanne) Medicine OBJECTIVE: To validate the reliability and efficiency of clinical diagnosis in practice based on a well-established system for the automatic segmentation of cerebral microbleeds (CMBs). METHOD: This is a retrospective study based on Magnetic Resonance Imaging-Susceptibility Weighted Imaging (MRI-SWI) datasets from 1,615 patients (median age, 56 years; 1,115 males, 500 females) obtained between September 2018 and September 2019. All patients had been diagnosed with cerebral small vessel disease (CSVD) with clear cerebral microbleeds (CMBs) on MRI-SWI. The patients were divided into training and validation cohorts of 1,285 and 330 patients, respectively, and another 30 patients were used for internal testing. The model training and validation data were labeled layer by layer and rechecked by two neuroradiologists with 15 years of work experience. Afterward, a three-dimensional convolutional neural network (CNN) was applied to the MRI data from the training and validation cohorts to construct a deep learning system (DLS) that was tested with the 72 patients, independent of the aforementioned MRI cohort. The DLS tool was used as a segmentation program for these 72 patients. These results were evaluated and revised by five neuroradiologists and subjected to an output analysis divided into the missed label, incorrect label, and correct label. The interneuroradiologists DLS agreement rate, which was assessed using the interrater agreement kappas test, was used for the quality analysis. RESULTS: In the detection and segmentation of the CMBs, the DLS achieved a Dice coefficient of 0.72. In the evaluation of the independent clinical data, the neuroradiologists reported that more than 90% of the lesions were directly detected and less than 10% of lesions were incorrectly labeled or the label was missed by our DLS. The kappa value for interneuroradiologist DLS agreement reached 0.79 on average. CONCLUSION: Based on the results, the automatic detection and segmentation of CMBs are feasible. The proposed well-trained DLS system might represent a trusted tool for the segmentation and detection of CMB lesions. Frontiers Media S.A. 2022-03-24 /pmc/articles/PMC8988858/ /pubmed/35402427 http://dx.doi.org/10.3389/fmed.2022.807443 Text en Copyright © 2022 Fan, Shan, Yang, Zheng, Wu, Chan, Wang, Gao, Liu, He and Sui. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Medicine
Fan, Pingping
Shan, Wei
Yang, Huajun
Zheng, Yu
Wu, Zhenzhou
Chan, Shang Wei
Wang, Qun
Gao, Peiyi
Liu, Yaou
He, Kunlun
Sui, Binbin
Cerebral Microbleed Automatic Detection System Based on the “Deep Learning”
title Cerebral Microbleed Automatic Detection System Based on the “Deep Learning”
title_full Cerebral Microbleed Automatic Detection System Based on the “Deep Learning”
title_fullStr Cerebral Microbleed Automatic Detection System Based on the “Deep Learning”
title_full_unstemmed Cerebral Microbleed Automatic Detection System Based on the “Deep Learning”
title_short Cerebral Microbleed Automatic Detection System Based on the “Deep Learning”
title_sort cerebral microbleed automatic detection system based on the “deep learning”
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8988858/
https://www.ncbi.nlm.nih.gov/pubmed/35402427
http://dx.doi.org/10.3389/fmed.2022.807443
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