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Acute ischemic stroke lesion segmentation in non-contrast CT images using 3D convolutional neural networks
A.V. Dobshik 1, S.K. Verbitskiy 1, I.A. Pestunov 1,2, K.M. Sherman 3, Yu.N. Sinyavskiy 2, A.A. Tulupov 3, V.B. Berikov 1,4

1 Novosibirsk State University, 630090, Russia, Novosibirsk, Pirogova street 1;
2 Federal Research Center for Information and Computational Technologies,
630090, Russia, Novosibirsk, Academician M.A. Lavrentiev avenue 6;
3 International Tomography Center SB RAS, 630090, Russia, Novosibirsk, Institutskaya str. 3a;
4 Sobolev Institute of Mathematics SB RAS, 630090, Russia, Novosibirsk, Academician Koptyuga avenue 4

 PDF, 1132 kB

DOI: 10.18287/2412-6179-CO-1233

Страницы: 770-777.

Язык статьи: English.

Аннотация:
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 &PlusMinus; 3.3 %, sensitivity of 69.9 &PlusMinus; 3.9 %, specificity of 99.7 &PlusMinus; 0.2 % and precision of 61.9 &PlusMinus; 3.6 %, showing promising segmentation results.

Ключевые слова:
ischemic stroke, brain, non-contrast CT, segmentation, CNN, 3D U-Net.

Благодарности
The work was partly supported by RFBR grant No. 19-29-01175, and by the State Contract of the Sobolev Institute of Mathematics, Project No. FWNF-2022-0015.

Citation:
Dobshik AV, Verbitskiy SK, Pestunov IA, Sherman KM, Sinyavskiy YuN, Tulupov AA, Berikov VB. Acute ischemic stroke lesion segmentation in non-contrast CT images using 3D convolutional neural networks. Computer Optics 2023; 47(5): 770-777. DOI: 10.18287/2412-6179-CO-1233.

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