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Research on the precise recognition stage of cerebral microhemorrhage based on deep learning algorithm
- 1 Beijing Jiaotong University
* Author to whom correspondence should be addressed.
Abstract
Cerebral microbleeds (CMB) is an important type of cerebral microbleeds. In recent years, many studies have proved that CMB can not only cause vascular dementia, but also increase the risk of stroke. Therefore, detection of CMB is of great clinical significance for balancing antithrombotic therapy and risk assessment in stroke patients, and detection of CMB is of great value for diagnosis and prognosis of cranial injury. This paper mainly proposes a two-stage CMB detection framework based on deep learning, which includes the screening stage of brain microhemorrhagic candidate points and the recognition stage of brain microhemorrhagic points based on deep learning. Firstly, in the first stage, we screened CMB candidate points by combining rapid radial transformation and threshold segmentation, and excluded a large number of background regions and obvious non-CMB regions. Then, in the second stage, the two-channel images spliced by sensitivity weighted imaging (SWI) and phase diagram (Pha) were used for false positive judgment by 3D convolutional neural network to distinguish the true CMB from the CMB analog.
Keywords
cerebral microbleeds, susceptibility weighted imaging, Convolutional neural network
[1]. Yan, Wu, Tao, & Chen. (2016). An up-to-date review on cerebral microbleeds. Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association, 25(6), 1301-6.
[2]. Haller, S. , Vernooij, M. W. , Kuijer, J. P. A. , Larsson, E. M. , & Barkhof, F. . (2018). Cerebral microbleeds: imaging and clinical significance. Radiology, 287(1), 11-28.
[3]. Haller, S. , Haacke, E. M. , Thurnher, M. M. , & Barkhof, F. . (2021). Susceptibility-weighted imaging: technical essentials and clinical neurologic applications. Radiology, 299(1), 203071.
[4]. Wilson D, Ambler G, Lee KI, Lim IS, Shiozawa M, Koga M, et al. (2019). Cerebralmicrobleeds and stroke risk after ischaemic stroke or transient ischaemic attack: apooled analysis ofindividual patient data from cohort studies. Lancet Neurol, 18:653-665.
[5]. He, K. , Zhang, X. , Ren, S. , & Sun, J. . (2015). Delving deep into rectifiers: surpassing human-level performance on imagenet classification. IEEE Computer Society.
Cite this article
Lv,P. (2024). Research on the precise recognition stage of cerebral microhemorrhage based on deep learning algorithm. Applied and Computational Engineering,88,1-8.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 6th International Conference on Computing and Data Science
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