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Detection of Cerebral Microbleeds in MR Images Using a Single-Stage Triplanar Ensemble Detection Network (TPE-Det)
Haejoon Lee1,2, Jun-Ho Kim1, Seul Lee1
1Department of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, Republic of Korea.
Background:
Cerebral microbleeds (CMBs) are microscopic brain hemorrhages with implications for various diseases. Automated detection of CMBs is a challenging task due to their wide distribution throughout the brain, small size, and visual similarity to their mimics. For this reason, most of the previously proposed methods have been accomplished through two distinct stages, which may lead to difficulties in integrating them into clinical workflows.
Purpose:
To develop a clinically feasible end-to-end CMBs detection network with a single-stage structure utilizing 3D information. This study proposes triplanar ensemble detection network (TPE-Det), ensembling 2D convolutional neural networks (CNNs) based detection networks on axial, sagittal, and coronal planes.
Study Type:
Retrospective.
Subjects:
Two datasets (DS1 and DS2) were used: 1) 116 patients with 367 CMBs and 12 patients without CMBs for training, validation, and testing (70.39 ± 9.30 years, 68 women, 60 men, DS1); 2) 58 subjects with 148 microbleeds and 21 subjects without CMBs only for testing (76.13 ± 7.89 years, 47 women, 32 men, DS2).
Field Strength/Sequence:
A 3 T field strength and 3D GRE sequence scan for SWI reconstructions.
Assessment:
The sensitivity, FPavg (false-positive per subject), and precision measures were computed and analyzed with statistical analysis.
Statistical Tests:
A paired t-test was performed to investigate the improvement of detection performance by the suggested ensembling technique in this study. A P value < 0.05 was considered significant.
Results:
The proposed TPE-Det detected CMBs on the DS1 testing set with a sensitivity of 96.05% and an FPavg of 0.88, presenting statistically significant improvement. Even when the testing on DS2 was performed without retraining, the proposed model provided a sensitivity of 85.03% and an FPavg of 0.55. The precision was significantly higher than the other models.
Data Conclusion:
The ensembling of multidimensional networks significantly improves precision, suggesting that this new approach could increase the benefits of detecting lesions in the clinic.
Evidence Level:
1 TECHNICAL EFFICACY: Stage 2.
Insights
This study introduces a novel triplanar ensemble detection network (TPE-Det) for accurate, single-stage detection of cerebral microbleeds (CMBs). The TPE-Det significantly improves detection precision, offering clinical benefits for diagnosing various brain diseases.
Area of Science:
- Medical imaging analysis
- Artificial intelligence in healthcare
- Neuroscience research
Background:
- Cerebral microbleeds (CMBs) are microscopic hemorrhages linked to various neurological conditions.
- Automated CMB detection is challenging due to their size, distribution, and mimicry.
- Existing two-stage methods hinder clinical workflow integration.
Purpose of the Study:
- To develop a clinically feasible, end-to-end CMB detection network using a single-stage, 3D-aware approach.
- Introduce the triplanar ensemble detection network (TPE-Det) by ensembling 2D CNNs across axial, sagittal, and coronal planes.
Main Methods:
- Retrospective analysis of two datasets (DS1: 116 patients, DS2: 58 subjects) with CMBs.
- Utilized 3T field strength and 3D GRE sequence for SWI reconstructions.
- Employed a paired t-test to assess the statistical significance of the ensembling technique (P < 0.05).
Main Results:
- TPE-Det achieved 96.05% sensitivity and 0.88 FPavg on DS1, with statistically significant improvement.
- On DS2 (without retraining), TPE-Det yielded 85.03% sensitivity and 0.55 FPavg.
- The proposed model demonstrated significantly higher precision compared to other methods.
Conclusions:
- Ensembling multidimensional networks significantly enhances CMB detection precision.
- The TPE-Det approach offers a promising advancement for clinical lesion detection.
- This method could improve diagnostic accuracy and patient outcomes in neurological disease management.
