Author:
Arab Ali,Chinda Betty,Medvedev George,Siu William,Guo Hui,Gu Tao,Moreno Sylvain,Hamarneh Ghassan,Ester Martin,Song Xiaowei
Abstract
Abstract
This project aimed to develop and evaluate a fast and fully-automated deep-learning method applying convolutional neural networks with deep supervision (CNN-DS) for accurate hematoma segmentation and volume quantification in computed tomography (CT) scans. Non-contrast whole-head CT scans of 55 patients with hemorrhagic stroke were used. Individual scans were standardized to 64 axial slices of 128 × 128 voxels. Each voxel was annotated independently by experienced raters, generating a binary label of hematoma versus normal brain tissue based on majority voting. The dataset was split randomly into training (n = 45) and testing (n = 10) subsets. A CNN-DS model was built applying the training data and examined using the testing data. Performance of the CNN-DS solution was compared with three previously established methods. The CNN-DS achieved a Dice coefficient score of 0.84 ± 0.06 and recall of 0.83 ± 0.07, higher than patch-wise U-Net (< 0.76). CNN-DS average running time of 0.74 ± 0.07 s was faster than PItcHPERFeCT (> 1412 s) and slice-based U-Net (> 12 s). Comparable interrater agreement rates were observed between “method-human” vs. “human–human” (Cohen’s kappa coefficients > 0.82). The fully automated CNN-DS approach demonstrated expert-level accuracy in fast segmentation and quantification of hematoma, substantially improving over previous methods. Further research is warranted to test the CNN-DS solution as a software tool in clinical settings for effective stroke management.
Funder
Simon Fraser University Community Trust Endowment Fund
Royal Columbian Hospital Foundation
Surrey Hospitals Foundation
Tianjin Medical University General Hospital Fellowship Award
Beijing Hospital Fellowship Award
Publisher
Springer Science and Business Media LLC
Reference58 articles.
1. Smith, S. D. & Eskey, C. J. Hemorrhagic stroke. Radiol. Clin. N. Am. 49, 27–45 (2011).
2. Heart and Stroke Foundation. Together against a rising tide: Advancing stroke systems of care. Stroke Month Report. https://www.heartandstroke.ca/-/media/pdf-files/canada/stroke-report/hsf-stroke-report-2014 (2014).
3. Feigin, V. L. et al. Global burden of stroke and risk factors in 188 countries, during 1990–2013: A systematic analysis for the Global Burden of Disease Study 2013. Lancet Neurol. 15, 913–924 (2016).
4. Feigin, V. L., Norrving, B. & Mensah, G. A. Global burden of stroke. Circ. Res. 120, 439–448 (2017).
5. Krishnamurthi, R. V. et al. The global burden of hemorrhagic stroke: A summary of findings from the GBD 2010 study. Glob. Heart. 9, 101–106 (2014).
Cited by
43 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献