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Assessing Cortical Cerebral Microinfarcts on High Resolution MR Images
Published on: November 20, 2015
Automatic Detection and Classification of Cerebral Microbleeds Using 3D CNN
M Mohsin Jadoon1,2, Victor Torres-Lopez3, Sharjeel A Butt2,4
1Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology (PAF-IAST), School of Computing Sciences, Pakistan.
Abstract:
Cerebral Microbleeds (CMBs) are referred to tiny foci of hemorrhage in brain parenchyma which are smaller than 5 (to 10) mm in size. The presence of CMBs is implicated in pathophysiology of cognitive impairment, dementia, radiation-induced vascular injury, traumatic brain injury, hypertensive microangiopathy, and aging. On brain Magnetic Resonance Imaging (MRI) scans, CMBs appear as hypointense foci, most notable on T2*-weighted or Susceptibility-Weighted Imaging (SWI). Detecting these tiny microbleeds with naked eye is a difficult and time-consuming task for radiologists. In this study we developed an algorithm for automatic detection of CMBs. We applied a two-step strategy: at first, we applied pre-processed 2D image dataset to You Only Look Once (YOLO V2) for detection of CMBs. Then, these detected CMBs locations are used to segment 3D patches from their original SWI volume in the datasets. Next, these patches are used as inputs for Convolution Neural Network (CNN). In the second step, we reduced the number of False Positives (FP) and improved our classification accuracy using 3D CNN. We used two datasets consisting of 979 patients: 879 of whom for training of models, and the remainder for independent validation. We were able to achieve an accuracy of 81% and reduce the to 0.16.
Insights
This study developed an automated algorithm for detecting cerebral microbleeds (CMBs) on MRI scans. The novel two-step approach achieved 81% accuracy, significantly aiding in diagnosing conditions linked to these brain hemorrhages.
Area of Science:
- Neuroimaging
- Artificial Intelligence
- Medical Diagnostics
Background:
- Cerebral Microbleeds (CMBs) are small hemorrhages (<10 mm) linked to cognitive decline, dementia, and brain injuries.
- Detecting CMBs on MRI scans (T2*-weighted, SWI) is challenging and time-consuming for radiologists.
- Accurate CMB detection is crucial for understanding and managing various neurological conditions.
Purpose of the Study:
- To develop and validate an automated algorithm for accurate cerebral microbleed detection.
- To improve the efficiency and reduce the subjectivity of CMB identification in neuroimaging.
- To enhance diagnostic capabilities for conditions associated with CMBs.
Main Methods:
- A two-step algorithm combining You Only Look Once (YOLO V2) for initial 2D detection and 3D Convolutional Neural Networks (CNNs) for refinement.
- Utilized pre-processed 2D image datasets for YOLO V2 detection, followed by 3D patch segmentation for CNN analysis.
- Trained and validated models on two datasets comprising 979 patients (879 for training, 100 for validation).
Main Results:
- Achieved an overall accuracy of 81% in detecting cerebral microbleeds.
- Successfully reduced the average false positive rate (FP_avg) to 0.16.
- Demonstrated the efficacy of the two-step YOLO V2 and 3D CNN approach in CMB identification.
Conclusions:
- The developed automated algorithm significantly improves cerebral microbleed detection accuracy and efficiency.
- This AI-driven approach offers a promising tool for radiologists in diagnosing and monitoring CMB-related neurological conditions.
- Further validation and implementation can enhance clinical workflows for neuroimaging analysis.
