Cerebral Microbleed Automatic Detection System Based on the "Deep Learning"

Pingping Fan; Wei Shan; Huajun Yang; Yu Zheng; Zhenzhou Wu; Shang Wei Chan; Qun Wang; Peiyi Gao; Yaou Liu; Kunlun He; Binbin Sui

doi:10.3389/fmed.2022.807443

Cerebral Microbleed Automatic Detection System Based on the "Deep Learning"

Front Med (Lausanne). 2022 Mar 24:9:807443. doi: 10.3389/fmed.2022.807443. eCollection 2022.

Authors

Pingping Fan^{1

2

3}, Wei Shan^{2

4

5}, Huajun Yang^{2

4}, Yu Zheng², Zhenzhou Wu², Shang Wei Chan², Qun Wang^{2

3

5}, Peiyi Gao¹, Yaou Liu^{1

2

4}, Kunlun He^{6

7}, Binbin Sui^{2

3}

Affiliations

¹ Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
² National Clinical Research Center for Neurological Diseases, Beijing, China.
³ Tiantan Neuroimaging Center of Excellence, Beijing, China.
⁴ Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
⁵ Beijing Institute for Brain Disorders, Beijing, China.
⁶ Laboratory of Translational Medicine, Chinese PLA General Hospital, Beijing, China.
⁷ Key Laboratory of Ministry of Biomedical Engineering and Translational Medicine, People's Liberation Army General Hospital, Beijing, China.

Abstract

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.

Keywords: cerebral microbleed; clinical evaluation; deep learning; neural network; segmentation.