Automated detection of cerebral microbleeds on T2*-weighted MRI

Chesebro, Anthony G.; Amarante, Erica; Lao, Patrick J.; Meier, Irene B.; Mayeux, Richard; Brickman, Adam M.

doi:10.1038/s41598-021-83607-0

Cited by 27 publications

(27 citation statements)

References 42 publications

(47 reference statements)

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“…Liu et al (2020) proposed to fuse the information in the space domain as well as the Fourier domain to generate the CMB candidates. Chesebro et al (2021) used a 2D gradient map and the circular Hough transform to obtain the initial CMBs and removed the false positive ones by entropy and blob analysis.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Cerebral Microbleed Detection via Convolutional Neural Network and Extreme Learning Machine

Liu

Wang

et al. 2021

Front. Comput. Neurosci.

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Aim: Cerebral microbleeds (CMBs) are small round dots distributed over the brain which contribute to stroke, dementia, and death. The early diagnosis is significant for the treatment.Method: In this paper, a new CMB detection approach was put forward for brain magnetic resonance images. We leveraged a sliding window to obtain training and testing samples from input brain images. Then, a 13-layer convolutional neural network (CNN) was designed and trained. Finally, we proposed to utilize an extreme learning machine (ELM) to substitute the last several layers in the CNN for detection. We carried out an experiment to decide the optimal number of layers to be substituted. The parameters in ELM were optimized by a heuristic algorithm named bat algorithm. The evaluation of our approach was based on hold-out validation, and the final predictions were generated by averaging the performance of five runs.Results: Through the experiments, we found replacing the last five layers with ELM can get the optimal results.Conclusion: We offered a comparison with state-of-the-art algorithms, and it can be revealed that our method was accurate in CMB detection.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Cerebral Microbleed Detection via Convolutional Neural Network and Extreme Learning Machine

Liu

Wang

et al. 2021

Front. Comput. Neurosci.

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“…It made full use of the spatial information of biomarkers and accelerated the computing speed. In addition to studies that used 2D/3D CNNs to detect CMBs on MRI-SWI, Chesebro et al ( 29 ) presented an algorithm for microbleed automated detection using geometric identification criteria (MAGIC) to detect CMBs automatically. It has reasonable precision on both T2*-weighted GRE images and SWI and had high sensitivity in longitudinal identification, with 50% of longitudinal microbleeds correctly labeled.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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ObjectiveTo validate the reliability and efficiency of clinical diagnosis in practice based on a well-established system for the automatic segmentation of cerebral microbleeds (CMBs).MethodThis 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.ResultsIn 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.ConclusionBased 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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“…2,3 Technically SWI provides contrast-enhanced images by exploiting the magnetic susceptibility differences in the tissues and has demonstrated excellent sensitivity in examination of the hemorrhage transformation in stroke and TBI patients 4 and cerebral microbleeds (CMBs) as well. 5 In addition, SWI can be applied together with other MRI techniques to examine amyloid-related imaging abnormalities and provide complementary information about the hemosiderin deposition in the brain as demonstrated by a recent study of aged squirrel monkey with cerebral amyloid angiopathy. 6 In contrast, QSM is a relatively new MRI technique and can be used to quantify the spatial distribution of magnetic susceptibility in the brain for separation of diamagnetic calcium from paramagnetic iron and quantification of iron deposition and blood byproducts.…”

mentioning

confidence: 99%

“…Neurological diseases like stroke, Parkinson's disease (PD), Alzheimer's diseases (AD), multiple sclerosis (MS) may alter the magnetic susceptibility of brain tissue due to abnormal iron and calcium deposition which can be monitored and assessed by using susceptibility weighted imaging (SWI) and quantitative susceptibility mapping (QSM) techniques in patients 1 and animals including chimpanzees 2,3 . Technically SWI provides contrast‐enhanced images by exploiting the magnetic susceptibility differences in the tissues and has demonstrated excellent sensitivity in examination of the hemorrhage transformation in stroke and TBI patients 4 and cerebral microbleeds (CMBs) as well 5 . In addition, SWI can be applied together with other MRI techniques to examine amyloid‐related imaging abnormalities and provide complementary information about the hemosiderin deposition in the brain as demonstrated by a recent study of aged squirrel monkey with cerebral amyloid angiopathy 6 .…”

mentioning

confidence: 99%

Editorial for “Cerebral Microbleeds Are Associated With Increased Brain Iron and Cognitive Impairment in Patients With Cerebral Small Vessel Disease—A Quantitative Susceptibility Mapping Study”

Zhang

2022

Magnetic Resonance Imaging

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Automated detection of cerebral microbleeds on T2*-weighted MRI

Cited by 27 publications

References 42 publications

RETRACTED: Cerebral Microbleed Detection via Convolutional Neural Network and Extreme Learning Machine

RETRACTED: Cerebral Microbleed Detection via Convolutional Neural Network and Extreme Learning Machine

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

Editorial for “Cerebral Microbleeds Are Associated With Increased Brain Iron and Cognitive Impairment in Patients With Cerebral Small Vessel Disease—A Quantitative Susceptibility Mapping Study”

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