2017 Winter Project Week/DeepInfer
- 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.
|Objective||Approach and Plan||Progress and Next Steps|
* Docker generates the XML of CLI * Slicer reads the XML (using QSlicerCLIModule) and generates the required GUI for the task * User selects input/output * The input images would be saved to a temporary directory * The docker will be run with the parameters on the mounted temporary directory * Slicer waits for completion of the task. * Docker saves the results of processing to the temp directory * Slicer loads the results.
* Implement instantiation of CLI UI from the XML description in Python. * Test the slicer/docker communication on our prostate-segmenter model.