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Efficient Segmentation of Medical Images Using Dilated Residual Networks

  • Lokeswara Rao BontaEmail author
  • N. Uday Kiran
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 31)

Abstract

Medical image segmentation is an essential part in many medical applications such as automatic measurement of tumour size, volume of organs and content-based image retrieval, etc. Various fully convolutional architectures have been proposed in the literature to tackle this problem. Lack of generalization is one of the main challenge in the field of medical imaging and all existing fully convolutional architectures involve huge number of parameters which make them prone to overfit the data. In this study, we proposed an efficient convolutional architecture called Dilated Residual Network (DRN) for medical image segmentation. By the design of DRN architecture, we have reduced number of parameters involved drastically, making the architecture less prone to overfitting hence by improving the generalization ability. We demonstrate that DRN outperforms the previous state of the art architecture called U-Net in medical image segmentation on various datasets in terms of training time, Dice score and Jaccard score. The source code (based on Keras with Tensorflow as the background) of the DRN and sample train and test image results are available at https://github.com/LokeshBonta/Dilated-Residual-Networks.

Keywords

Medical image segmentation Fully convolutional neural networks Dilated convolutions 

References

  1. 1.
    He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778Google Scholar
  2. 2.
    Kaggle (2015) Ultrasound nerve segmentation dataGoogle Scholar
  3. 3.
    Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105Google Scholar
  4. 4.
    Radau P, Lu Y, Connelly K, Paul G, Dick A, Wright G (2009) Evaluation framework for algorithms segmenting short axis cardiac MRI. MIDAS J-Card MR Left Ventricle Segm Chall 49Google Scholar
  5. 5.
    Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 234–241Google Scholar
  6. 6.
    Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
  7. 7.
    Tran PV (2016) A fully convolutional neural network for cardiac segmentation in short-axis MRI. arXiv:1604.00494
  8. 8.
    Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. arXiv:1511.07122

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Sri Sathya Sai Institute of Higher LearningAnantapurIndia

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