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Cited by 679Medical Image Segmentation TechniquesIntracerebral and Subarachnoid Hemorrhage ResearchBrain Tumor Detection and Classification

The DOI matches its Crossref record; 49 of the 51 references it cites were found in a registry or an index.

Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks

Dou, Qi, Chen, Hao, Yu, Lequan, Zhao, Lei, Qin, Jing, Wang, Defeng, Mok, Vincent CT, Shi, Lin, Heng, Pheng-Ann·2016·IEEE Transactions on Medical Imaging
DOI 10.1109/tmi.2016.2528129
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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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Research works printed but not linked: 4

  1. bengio, 2015, Deep Learning
  2. prasoon, 2013, Medical Image Computing and Comput -Assisted Intervention-MICCAI 2013, 246
  3. roth, 2014, Medical Image Computing and Comput -Assisted Intervention-MICCAI, 520
  4. urban, 2014, Multi-modal brain tumor segmentation using deep convolutional neural networks, Proc Winning Contribution MICCAI BraTS (Brain Tumor Segmentation) Challenge, 31

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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)
ORCID
Venue
IEEE Transactions on Medical Imaging
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2016-05
Volume
35
Issue
5
Pages
1182-1195
ISSN
  • 0278-0062 (ISSN-L)
  • 1558-254X
DOI
10.1109/tmi.2016.2528129
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
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
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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