AI Article Synopsis
- Cerebral microbleeds (CMBs) are marks of severe brain small vessel disease identifiable via MRI, where a two-stage deep learning pipeline was designed to automate their detection using quantitative susceptibility mapping (QSM).
- The study reviewed data from 393 patients with a total of 1843 CMBs, utilizing a combination of algorithms to discern CMBs from other similar structures in brain images.
- The pipeline showed high detection sensitivities (up to 94.9%) and a low false positive rate (2.87 per subject), demonstrating effectiveness in identifying CMBs across various brain regions while implementing a semi-automated scoring system.
Article Abstract
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.
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| http://dx.doi.org/10.1002/jmri.29198 | DOI Listing |
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