Advertisement

Towards Integrating ImageJ with Deep Biomedical Models

  • Adrián InésEmail author
  • César Domínguez
  • Jónathan Heras
  • Eloy Mata
  • Vico Pascual
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 801)

Abstract

Nowadays, deep learning techniques are playing an important role in different areas due to the fast increase in both computer processing capacity and availability of large amount of data. Their applications are diverse in the field of bioimage analysis, e.g. for classifying and segmenting microscopy images, for automating the localization of proteins or for automating brain MRI segmentation. Our goal in this project consists in including these deep learning techniques in ImageJ – one of the most used image processing programs in this research community. To do this, we want to develop an ImageJ plugin from which to use the models and functionalities of the main deep learning frameworks (such as Caffe, Keras or Tensorflow). It would be feasible to test the suitability of different models to the problem that is being studied at each moment, avoiding the problems of interoperability among different frameworks. As a first step, we will define an API that allows the invocation of deep models for object classification from several frameworks; and, subsequently, we will develop an ImageJ plugin to make the use of such an API easier.

Keywords

Bioimage Deep learning Image processing ImageJ Interoperability Object classification 

References

  1. 1.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation (2015). http://arxiv.org/abs/1505.04597Google Scholar
  2. 2.
    Nauman, M., Ur Rehman, H., Politano, G., Benso, A.: Beyond homology transfer: deep learning for automated annotation of proteins. bioRxiv (2017). https://www.biorxiv.org/content/early/2017/07/25/168120
  3. 3.
    Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D.L., Erickson, B.J.: Deep learning for brain MRI segmentation: state of the art and future directions. J. Digit. Imaging 30(4), 449–459 (2017)CrossRefGoogle Scholar
  4. 4.
    Rueden, C.T., Eliceiri, K.W.: The ImageJ ecosystem: an open and extensible platform for biomedical image analysis. Microsc. Microanal. 23(S1), 226–227 (2017)CrossRefGoogle Scholar
  5. 5.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093 (2014)
  6. 6.
    Chollet, F., et al.: Keras (2015). https://github.com/keras-team/keras
  7. 7.
    Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous distributed systems (2016). http://arxiv.org/abs/1603.04467
  8. 8.
    Eclipse Deeplearning4J Development Team: Deeplearning4j: Open-source, Distributed Deep Learning for the JVM (2018). https://deeplearning4j.org/
  9. 9.
    Google Brain team: ImageJ/TensorFlow integration library plugin (2018). https://imagej.net/TensorFlow
  10. 10.
    Yuan, S.: Deep learning model convertors (2018). https://github.com/ysh329/deep-learning-model-convertor
  11. 11.
    Microsoft, Facebook open source & AWS: ONNX: Open Neural Network Exchange (2018). http://onnx.ai/

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Adrián Inés
    • 1
    Email author
  • César Domínguez
    • 1
  • Jónathan Heras
    • 1
  • Eloy Mata
    • 1
  • Vico Pascual
    • 1
  1. 1.Dpto. Matemáticas y ComputaciónUniversidad de La RiojaLogroñoSpain

Personalised recommendations