Abstract
In this work, research was focused on the recognition of stroke lesions in FLAIR MRI images. The main tools used in the study were convolution neural networks (CNN) and histograms of oriented gradients (HOG). To train the neural network, 706 FLAIR MRI images of real patients were collected and labeled. During the testing of the program implementing the algorithm, it was found out that the histogram of oriented gradients method was ineffective in clarifying the edges of the lesion. To replace the HOG technology, we proposed a method on calculating the average pixel intensity in the lesion area. The result is a program that shows an average value of 0.554 by Dice score. The developed program can be used as a digital assistant for physicians who identify the lesions of stroke on MRI images and for training physicians in radiology and neurology. With the help of the developed technology it is possible to reduce the time of detection of the stroke lesions, reduce variability of results, increase their reproducibility and process a huge amount of data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Boldsen, J., Engedel, T., Pedraza, S., Cho, T., Thomalla, G., Nighoghossian, N., Baron, J., Fiehler, J., Ostergaard, L., Mouridsen, K.: Better Diffusion Segmentation in Acute Ischemic Stroke Through Automatic Tree Learning Anomaly Segmentation. https://www.frontiersin.org/articles/10.3389/fninf.2018.00021/full. Accessed 25 Feb 2020
Chen, L., Bentley, P., Rueckert, D.: Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks. NeuroImage Clin. 15, 633–643 (2017)
Girshick, R.: Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, pp. 1440–1448 (2015). https://doi.org/10.1109/ICCV.2015.169. Fg
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, pp. 580–587 (2014). https://doi.org/10.1109/CVPR.2014.81. Fg
LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989). https://doi.org/10.1162/neco.1989.1.4.541
Maier, O., Schröder, C., Forkert, N.D., Martinetz, T., Handels, H.: Classifiers for ischemic stroke lesion segmentation: a comparison study. PLoS ONE 10(12), e0145118 (2015). https://doi.org/10.1371/journal.pone.0145118. Published correction appears in PLoS One. 2016;11(2):e0149828
Nielsen, M.: Neural Network and Deep Learning. Determination Press, Brisbane (2015)
Nikolenko, S., Kadurin, A., Arkhangelskaya, E.: Глyбoкoe oбyчeниe. Пoгpyжeниe в миp нeйpoнныx ceтeй. Питep (2017)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, pp. 779–788 (2016). https://doi.org/10.1109/CVPR.2016.91
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017). https://doi.org/10.1109/TPAMI.2016.2577031
Subbanna, N., Rajashekar, D., Cheng, B., Thomalla, G., Fiehler, J., Arbel, T., Forkert, N.: Stroke Lesion Segmentation in FLAIR MRI Datasets Using Customized Markov Random Fields. https://www.ncbi.nlm.nih.gov/pubmed/31178820. Accessed 04 Dec 2019
Woo, I., Lee, A., Jung, S.C., Lee, H., Kim, N., Cho, S.J., Kim, D., Lee, J., Sunwoo, L., Kang, D.-W.: Fully automatic segmentation of acute ischemic lesions on diffusion-weighted imaging using convolutional neural networks: comparison with conventional algorithms. Korean J. Radiol. 20(8) 1275–1284 (2019)
Zhang, R., Zhao, L., Lou, W., Abrigo, J., Mok, V., Chu, W., Wang, D., Shi, L.: Automatic segmentation of acute ischemic stroke from DWI Using 3-D fully convolutional DenseNets. IEEE Trans. Med. Imaging 37, 2149–2160 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mamedov, N., Kulikova, S., Drobakha, V., Bartuli, E., Ragachev, P. (2021). Automatic Segmentation of Acute Stroke Lesions Using Convolutional Neural Networks and Histograms of Oriented Gradients. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research IV. NEUROINFORMATICS 2020. Studies in Computational Intelligence, vol 925. Springer, Cham. https://doi.org/10.1007/978-3-030-60577-3_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-60577-3_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60576-6
Online ISBN: 978-3-030-60577-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)