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U-ISLES: Ischemic Stroke Lesion Segmentation Using U-Net

  • Lea Katrina S. Cornelio
  • Mary Abigail V. del Castillo
  • Prospero C. Naval Jr.
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 869)

Abstract

An accurate automation of image segmentation has a great significance in enhancing the processing of biomedical images. This could avoid the tedious task of manually segmenting biomedical images and the data inconsistencies from the varying configurations in MRI scans from hospitals. In this study, a new method in segmenting ischemic stroke lesions is proposed. The dataset is downloaded from Ischemic Stroke Lesion Segmentation 2017 (ISLES 2017) Challenge. It consists of MRI scan images of the brain from the patients. The preprocessing of data involves the application of batch normalization, image resizing, color conversion, and contrast correction. The proposed deep learning technique is a type of a two-dimensional CNN known as U-Net. Experiments are performed to determine the combination of loss function and regularizer which returns the optimal result. The results of different contrast intensities are also compared among themselves. After experimentation and segmentation, the output is post-processed to eliminate isolated small regions and smooth lesion edges. The metric used to measure the accuracy of the model is dice score.

Keywords

Image segmentation Ischemic stroke U-net Biomedical imaging 

References

  1. 1.
    Zhao, L., Jhiya, K.: Multiscale CNNs for brain tumor segmentation and diagnosis. Comput. Math. Methods Med. 2016(Article ID 8356294), 7 (2016).  https://doi.org/10.1155/2016/8356294MathSciNetCrossRefGoogle Scholar
  2. 2.
    Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P., Larochelle, H.: Brain Tumor Segmentation with Deep Neural Networks. Cornell University Library (2016). arXiv:1505.03540v3,  https://doi.org/10.1016/j.media.2016.05.004CrossRefGoogle Scholar
  3. 3.
    Ronneberger, O., Fischer, H., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer Assisted Intervention (MICCAI) (2015)Google Scholar
  4. 4.
    Sevastopolsky, A.: Optic Disc and Cup Segmentation Methods for Glaucoma Detection with Modification of U-Net Convolutional Neural Network. Cornell University Library (2017). arXiv:1704.00979v1
  5. 5.
    Loch, F.: Image Processing Algorithms Part 5: Contrast Adjustment—Dreamland Fantasy Studios. Dreamland Fantasy Studios (2018). http://www.dfstudios.co.uk/articles/programming/image-programming-algorithms/image-processing-algorithms-part-5-contrast-adjustment/
  6. 6.
    Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15 (2014)Google Scholar
  7. 7.
    Ioffe, S., Szegedy, C., Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Cornell University Library (2015). arXiv:1502.03167v3
  8. 8.
    Maier, O., et al.: ISLES 2015—A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRIGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Lea Katrina S. Cornelio
    • 1
  • Mary Abigail V. del Castillo
    • 1
  • Prospero C. Naval Jr.
    • 1
  1. 1.Department of Computer ScienceComputer Vision & Machine Intelligence Group, College of Engineering, University of the PhilippinesDilimanPhilippines

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