2017 Winter Project Week/DeepInfer

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  Projects List

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  The initial prototype of the DeepInfer with OpenIGTLink.

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.