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This article is cited in 3 scientific papers (total in 3 papers)
IMAGE PROCESSING, PATTERN RECOGNITION
Acute ischemic stroke lesion segmentation in non-contrast CT images using 3D convolutional neural networks
A. V. Dobshika, S. K. Verbitskiya, I. A. Pestunovab, K. M. Shermanc, Yu. N. Sinyavskiyb, A. A. Tulupovc, V. B. Berikovad a Novosibirsk State University
b Federal Research Center for Information and Computational Technologies
c International Tomography Center of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk
d Sobolev Institute of Mathematics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk
Abstract:
In this paper, an automatic algorithm aimed at volumetric segmentation of acute ischemic stroke lesion in non-contrast computed tomography brain 3D images is proposed. Our deep-learning approach is based on the popular 3D U-Net convolutional neural network architecture, which was modified by adding the squeeze-and-excitation blocks and residual connections. Robust pre-processing methods were implemented to improve the segmentation accuracy. Moreover, a special patches sampling strategy was used to address the large size of medical images and class imbalance and to stabilize neural network training. All experiments were performed using five-fold cross-validation on the dataset containing non-contrast computed tomography volumetric brain scans of 81 patients diagnosed with acute ischemic stroke. Two radiology experts manually segmented images independently and then verified the labeling results for inconsistencies. The quantitative results of the proposed algorithm and obtained segmentation were measured by the Dice similarity coefficient, sensitivity, specificity and precision metrics. The suggested pipeline provides a Dice improvement of $12.0\%$, sensitivity of $10.2\%$ and precision $10.0\%$ over the baseline and achieves an average Dice of $62.8\pm 3.3\%$, sensitivity of $69.9\pm 3.9\%$, specificity of $99.7\pm 0.2\%$ and precision of $61.9\pm 3.6\%$, showing promising segmentation results.
Keywords:
ischemic stroke, brain, non-contrast CT, segmentation, CNN, 3D U-Net
Received: 01.10.2022 Accepted: 04.04.2023
Citation:
A. V. Dobshik, S. K. Verbitskiy, I. A. Pestunov, K. M. Sherman, Yu. N. Sinyavskiy, A. A. Tulupov, V. B. Berikov, “Acute ischemic stroke lesion segmentation in non-contrast CT images using 3D convolutional neural networks”, Computer Optics, 47:5 (2023), 770–777
Linking options:
https://www.mathnet.ru/eng/co1178 https://www.mathnet.ru/eng/co/v47/i5/p770
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