NA-MIC Project Weeks

Back to Projects List

Live tracked ultrasound processing with PyTorch

Key Investigators

• Tamas Ungi (Queen's University)
• Colton Barr (Queen's University / BWH)
• Tina Kapur (Brigham and Women's Hospital)

Project Description

Our past code for training and deploying ultrasound segmentation in real time was based on TensorFlow. Example project: https://youtu.be/WyscpAee3vw

The goal for this project week is to provide a new open-source implementation using PyTorch and modern AI tools like MONAI and wandb. A Slicer module will also be provided to deploy trained AI on recorded or live ultrasound streams.

Objective

1. Export annotated ultrasound+tracking data for training
2. Example code for training
3. Slicer module to use trained models on ultrasound data in Slicer

Approach and Plan

1. All data processing and training code will be here: https://github.com/SlicerIGT/aigt/tree/master/UltrasoundSegmentation
2. Slicer module will be here: https://github.com/SlicerIGT/aigt/tree/master/SlicerExtension/LiveUltrasoundAi/TorchLiveUs

Progress and Next Steps

1. Describe specific steps you have actually done.

Illustrations

No response

Background and References

No response