2017 Winter Project Week/DeepInfer

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Home < 2017 Winter Project Week < DeepInfer


Key Investigators

  • Alireza Mehrtash (BWH, UBC)
  • Mehran Pesteie (UBC)
  • Yang (Silvia) Yixin (Tianjin University)
  • Tina Kapur (BWH)
  • Sandy Wells (BWH)
  • Purang Abolmaesumi (UBC)
  • Andriy Fedorov (BWH)

Background and References

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. However, utilizing deep models during image-guided therapy procedures requires integration of several software components which is often a tedious taskfor clinical researchers. Hence, there is a gap between the state-of-the-art machine learning research and itsapplication in clinical setup.

DeepInfer enables 3D Slicer to connect to a powerful processing back-end either on the local machine or a remote processing server. Utilizing a repository of pre-trained, task-specific models, DeepInfer allows clinical researchers and biomedical engineers to choose and deploy a model on new data without the need for software development or configuration.

Project Description

Objective Approach and Plan Progress and Next Steps
  • Redesign the architecture of the toolkit considering Docker as the deep learning model deployment engine.
  • Discuss about the implementation details of Slicer side.
  • Planning the structure of the cloud model repository.
  • Study and evaluate different approaches for passing input images and prediction results between Slicer and Docker including CLI.
  • Decide about the necessary fields in the metadata of the stored models.
  • Implement Slicer side to talk with Docker.