Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Segmentation of 3D Non-Contrast CT Brain Images Using Transformer Neural Networks. / Kirillov, Kirill; Mikhailapov, Denis; Tulupov, Andrey et al.
2024 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2024. Institute of Electrical and Electronics Engineers Inc., 2024. p. 173-177 (2024 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2024).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
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TY - GEN
T1 - Segmentation of 3D Non-Contrast CT Brain Images Using Transformer Neural Networks
AU - Kirillov, Kirill
AU - Mikhailapov, Denis
AU - Tulupov, Andrey
AU - Berikov, Vladimir
N1 - The work was partly supported by the State Contract of the Sobolev Institute of Mathematics, Project No. FWNF-2022-0015.
PY - 2024/11/26
Y1 - 2024/11/26
N2 - We propose a method for semantic segmentation of 3D non-contrast computed tomography brain images of acute ischemic stroke using transformer neural networks. To improve the segmentation quality of lesion areas, the pre-processing methods were implemented. The 3D Swin UNETR model is employed for segmentation, which is based on the attention mechanism. The sum of DICE loss and Focal loss are used to train the model, and DICE score as well as sensitivity and precision is utilized to evaluate the quality of model's predictions. The model was trained and tested using cross-validation on real images of patients at the International Tomography Center SB RAS. Research and comparison of the performance of the model and its analogues was carried out. The proposed algorithm demonstrates 30% greater DICE metric in comparison with the analogous 3D U-Net model. The main feature of the 3D Swin UNETR model is the increase in false positives and the decrease in false negatives compared to 3D U-Net.
AB - We propose a method for semantic segmentation of 3D non-contrast computed tomography brain images of acute ischemic stroke using transformer neural networks. To improve the segmentation quality of lesion areas, the pre-processing methods were implemented. The 3D Swin UNETR model is employed for segmentation, which is based on the attention mechanism. The sum of DICE loss and Focal loss are used to train the model, and DICE score as well as sensitivity and precision is utilized to evaluate the quality of model's predictions. The model was trained and tested using cross-validation on real images of patients at the International Tomography Center SB RAS. Research and comparison of the performance of the model and its analogues was carried out. The proposed algorithm demonstrates 30% greater DICE metric in comparison with the analogous 3D U-Net model. The main feature of the 3D Swin UNETR model is the increase in false positives and the decrease in false negatives compared to 3D U-Net.
KW - ischemic stroke
KW - neural networks
KW - non-contrast CT
KW - semantic segmentation
KW - swin-transformer
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85212142837&origin=inward&txGid=d1ca057b68ae387e31b9e901039e847e
UR - https://www.mendeley.com/catalogue/f81dbba8-b606-3870-bb54-8b290b99c752/
U2 - 10.1109/SIBIRCON63777.2024.10758478
DO - 10.1109/SIBIRCON63777.2024.10758478
M3 - Conference contribution
SN - 9798331532024
T3 - 2024 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2024
SP - 173
EP - 177
BT - 2024 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Multi-Conference on Engineering, Computer and Information Sciences
Y2 - 30 September 2024 through 2 November 2024
ER -
ID: 61787556