Deep‐Learning‐Based MRI Microbleeds Detection for Cerebral Small Vessel Disease on Quantitative Susceptibility Mapping - Xia, Peng; Hui, Edward S.; Chua, Bryan J.; Huang, Fan; Wang, Zuojun; Zhang, Huiqin; Yu, Han; Lau, Kui Kai; Mak, Henry K.F.; Cao, Peng (Journal of magnetic resonance imaging, 2024)
Background Cerebral microbleeds (CMB) are indicators of severe cerebral small vessel disease (CSVD) that can be identified through hemosiderin‐sensitive sequences in MRI. Specifically, quantitative susceptibility mapping (QSM) and deep learning were applied to detect CMBs in MRI. Purpose To automatically detect CMB on QSM, we proposed a two‐stage deep learning pipeline. Study Type Retrospective. Subjects A total number of 1843 CMBs from 393 patients (69 ± 12) with cerebral small vessel disease were included in this study. Seventy‐eight subjects (70 ± 13) were used as external testing. Field Strength/Sequence 3 T/QSM. Assessment The proposed pipeline consisted of two stages. In stage I, 2.5D fast radial symmetry transform (FRST) algorithm along with a one‐layer convolutional network was used to identify CMB candidate regions in QSM images. In stage II, the V‐Net was utilized to reduce false positives. The V‐Net was trained using CMB and non CMB labels, which allowed for high‐level feature extraction and differentiation between CMBs and CMB mimics like vessels. The location of CMB was assessed according to the microbleeds anatomical rating scale (MARS) system. Statistical Tests The sensitivity and positive predicative value (PPV) were reported to evaluate the performance of the model. The number of false positive per subject was presented. Results Our pipeline demonstrated high sensitivities of up to 94.9% at stage I and 93.5% at stage II. The overall sensitivity was 88.9%, and the false positive rate per subject was 2.87. With respect to MARS, sensitivities of above 85% were observed for nine different brain regions. Data Conclusion We have presented a deep learning pipeline for detecting CMB in the CSVD cohort, along with a semi‐automated MARS scoring system using the proposed method. Our results demonstrated the successful application of deep learning for CMB detection on QSM and outperformed previous handcrafted methods. Level of Evidence 2 Technical Efficacy Stage 2