Abstract
The diagnosis of stroke, a leading cause of death in the world, using computed tomography (CT) scans, makes it possible to assess the severity of the incident and to determine the type and location of the lesion. The fact that the brain has two hemispheres with a high level of anatomical similarity, exhibiting significant symmetry, has led to extensive research based on the assumption that a decrease in symmetry is directly related to the presence of pathologies. This work is focused on the analysis of the symmetry (or lack of it) of the two brain hemispheres, and on the use of this information for the classification of computed tomography brain scans of stroke patients. The objective is to contribute to a more precise diagnosis of brain lesions caused by ischemic stroke events. To perform this task, we used a siamese network architecture that receives a double two-dimensional image of a CT slice (the original and a mirrored version) and a label that reflects the existence or not of a visible stroke event. The network then extracts the relevant features and can be used to classify brain-CT slices taking into account their perceived symmetry. The best performing network exhibits an average accuracy and F1-score of 72%, when applied to CT slices of previously unseen patients, significantly outperforming two state-of-the-art convolutional network architectures, which were used as baselines. When applied to slices chosen randomly, that may or may not be from the same patient, the network exhibits an accuracy of 97%, but this performance is due in part to overfitting, as the system is able to learn specific features of each patient brain.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Code available at: https://github.com/anagilvieira/siamese_network.git.
References
Barman, A., Inam, M.E., Lee, S., Savitz, S., Sheth, S., Giancardo, L.: Determining ischemic stroke from ct-angiography imaging using symmetry-sensitive convolutional networks. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 1873–1877. IEEE (2019)
Barzegar, Z., Jamzad, M.: Fully automated glioma tumour segmentation using anatomical symmetry plane detection in multimodal brain MRI. IET Comput. Vis. 15(7), 463–473 (2021)
Chen, L., Bentley, P., Rueckert, D.: Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks. NeuroImage Clin. 15, 633–643 (2017)
Chin, C.L., et al.: An automated early ischemic stroke detection system using CNN deep learning algorithm. In: 2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST), pp. 368–372. IEEE (2017)
Dey, S., Dutta, A., Toledo, J.I., Ghosh, S.K., Lladós, J., Pal, U.: SigNet: convolutional siamese network for writer independent offline signature verification. CoRR abs/1707.0 (2017)
Gibicar, A., Moody, A.R., Khademi, A.: Automated midline estimation for symmetry analysis of cerebral hemispheres in FLAIR MRI. Front. Aging Neurosci. 13, 644137 (2021)
Goldman, L.W.: Principles of CT and CT technology. J. Nucl. Med. Technol. 35(3), 115–128 (2007)
Herzog, N.J., Magoulas, G.D.: Brain asymmetry detection and machine learning classification for diagnosis of early dementia. Sensors 21(3), 778 (2021)
Lindsay, M.P., et al.: World Stroke Organization (WSO): global stroke fact sheet 2019. Int. J. Stroke 14(8), 806–817 (2019)
Liu, S.X.: Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: a review of the literature. J. Biomed. Inf. 42(6), 1056–1064 (2009)
Liu, X., Zhou, Y., Zhao, J., Yao, R., Liu, B., Zheng, Y.: Siamese convolutional neural networks for remote sensing scene classification. IEEE Geosci. Remote Sens. Lett. 16(8), 1200–1204 (2019)
Melekhov, I., Kannala, J., Rahtu, E.: Siamese network features for image matching. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 378–383. IEEE (2016)
Miletto Petrazzini, M.E., Sovrano, V.A., Vallortigara, G., Messina, A.: Brain and behavioral asymmetry: a lesson from fish. Front. Neuroanatomy 14, 11 (2020)
Öman, O., Mäkelä, T., Salli, E., Savolainen, S., Kangasniemi, M.: 3D convolutional neural networks applied to CT angiography in the detection of acute ischemic stroke. Eur. Radiol. Exp. 3(1), 8 (2019)
Phipps, M.S., Cronin, C.A.: Management of acute ischemic stroke. BMJ 368, l6983 (2020)
Pohl, M., et al.: Ischemic stroke mimics: a comprehensive review. J. Clin. Neurosci. 93, 174–182 (2021)
Qiu, K., Ai, Y., Tian, B., Wang, B., Cao, D.: Siamese-ResNet: implementing loop closure detection based on siamese network. In: 2018 IEEE Intelligent Vehicles Symposium (IV), pp. 716–721. IEEE (2018)
Ribolsi, M., Daskalakis, Z.J., Siracusano, A., Koch, G.: Abnormal asymmetry of brain connectivity in schizophrenia. Front. Human Neurosci. 8, 1010 (2014)
Ruppert, G.C.S., Teverovskiy, L., Yu, C.P., Falcao, A.X., Liu, Y.: A new symmetry-based method for mid-sagittal plane extraction in neuroimages. In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 285–288. IEEE (2011)
Vupputuri, A., Dighade, S., Prasanth, P.S., Ghosh, N.: Symmetry determined superpixels for efficient lesion segmentation of ischemic stroke from MRI. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 742–745. IEEE (2018)
Wu, H., Chen, X., Li, P., Wen, Z.: Automatic symmetry detection from brain MRI based on a 2-channel convolutional neural network. IEEE Trans. Cybern. 51(9), 4464–4475 (2021)
Xue, Z., Antani, S., Long, L.R., Demner-Fushman, D., Thoma, G.R.: Window classification of brain CT images in biomedical articles. In: AMIA Annual Symposium proceedings 2012, pp. 1023–1029 (2012)
Yu, C.P., Ruppert, G., Nguyen, D., Falcão, A., Liu, Y.: Statistical asymmetry-based brain tumor segmentation from 3D MR images. In: Biosignals (2012)
Acknowledgments
This research was supported by the Portuguese Science Foundation, through the Projects PRELUNA - PTDC/CCI-INF/4703/2021 and UIDB/50021/2020.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Vieira, A.B., Fonseca, A.C., Ferro, J., Oliveira, A.L. (2022). Using a Siamese Network to Accurately Detect Ischemic Stroke in Computed Tomography Scans. In: Marreiros, G., Martins, B., Paiva, A., Ribeiro, B., Sardinha, A. (eds) Progress in Artificial Intelligence. EPIA 2022. Lecture Notes in Computer Science(), vol 13566. Springer, Cham. https://doi.org/10.1007/978-3-031-16474-3_14
Download citation
DOI: https://doi.org/10.1007/978-3-031-16474-3_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16473-6
Online ISBN: 978-3-031-16474-3
eBook Packages: Computer ScienceComputer Science (R0)