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Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks
Abstract
Cerebral microbleeds (CMBs) are small haemorrhages nearby blood vessels. They have been recognized as important diagnostic biomarkers for many cerebrovascular diseases and cognitive dysfunctions. In current clinical routine, CMBs are manually labelled by radiologists but this procedure is laborious, time-consuming, and error prone. In this paper, we propose a novel automatic method to detect CMBs from magnetic resonance (MR) images by exploiting the 3D convolutional neural network (CNN). Compared with previous methods that employed either low-level hand-crafted descriptors or 2D CNNs, our method can take full advantage of spatial contextual information in MR volumes to extract more representative high-level features for CMBs, and hence achieve a much better detection accuracy. To further improve the detection performance while reducing the computational cost, we propose a cascaded framework under 3D CNNs for the task of CMB detection. We first exploit a 3D fully convolutional network (FCN) strategy to retrieve the candidates with high probabilities of being CMBs, and then apply a well-trained 3D CNN discrimination model to distinguish CMBs from hard mimics. Compared with traditional sliding window strategy, the proposed 3D FCN strategy can remove massive redundant computations and dramatically speed up the detection process. We constructed a large dataset with 320 volumetric MR scans and performed extensive experiments to validate the proposed method, which achieved a high sensitivity of 93.16% with an average number of 2.74 false positives per subject, outperforming previous methods using low-level descriptors or 2D CNNs by a significant margin. The proposed method, in principle, can be adapted to other biomarker detection tasks from volumetric medical data.
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“They reported their method outperformed previous studies with low level descriptors and provided a high sensitivity of 93.2% for detecting CM.”
From Deep learning for brain MRI segmentation: state of the art and future directions (2017) · cites this paper as [32]
In the citing paper
“Cascaded architectures such as a patch-wise architecture following a semantic architecture as used in [32] would resolve the issues raised by each approach and refine the output results.”
From Deep learning for brain MRI segmentation: state of the art and future directions (2017) · cites this paper as [32]
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Metadata
- Authors
- Dou, Q., Chen, H., Yu, L., Zhao, L., Qin, J., Wang, D., Mok, V. C., Shi, L., & Heng, P.-A.
- Affiliations
- Chinese University of Hong Kong (HK)
- Shenzhen University (CN)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2016-05
- Volume
- 35
- Issue
- 5
- Pages
- 1182-1195
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2343172899
- Cited by
- 679
- References
- 51
- Citations by year
- 2026: 23 · 2025: 26 · 2024: 40 · 2023: 84 · 2022: 71 · 2021: 78 · 2020: 88 · 2019: 130 · 2018: 76 · 2017: 47 · 2016: 14
- FWCI
- 60.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Clinical Neurology › Intracerebral and Subarachnoid Hemorrhage Research
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
- Keywords
- cerebral microbleeds, automated detection, 3D convolutional neural networks, fully convolutional network, false positive reduction
- MeSH terms
- Brain, Cerebral Hemorrhage, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Biomarkers, Databases, Factual, Neural Networks, Computer, Imaging, Three-Dimensional
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