Project: New Radiology and Pathology Deep Learning Models into MHub.ai

Draft Status

Ready - team will start page creating immediately

Presenter Location

In-person

Key Investigators

Curtis Lisle (KnowledgeVis, USA)
Leonard Nürnberg (Brigham and Women's Hospital, Boston)

Project Description

The MHub.ai project at Harvard has developed methods to execute machine learning models on medical images in an easy to use and standardized way. There is already a Slicer plugin for running MHub.ai format models. For this project, we propose to add two models of row different types to the MHub library.

Objective

Objective A. Test a MONAI-based deep learning model in MHub and validate the instructions for new developers to follow.
Objective B. Evaluate how well the MHub approach works for supporting pathology models in addition to radiology models.

Approach and Plan

Step 1. Port one of the pre-trained MONAIAutoSeg3D radiology models developed at Queens (by Andros Lasso et al.) for execution using the MHub framework as a docker container. Test the MHub I/O converters to read a DICOM image and reformat as needed from the input. Write out a DICOM segmentation object as the result.

Step 2. Start converting a published pathology DNN model (Rhabdomyosarcoma segmentation) for the MHub framework. This will Evaluate how well the MHub approach works for supporting pathology models in addition to radiology models. For example, can the same base Docker image work for pathology?

Progress and Next Steps

Describe specific steps you have actually done.

Illustrations

No response

Background and References

MONAI AutoSeg3D: https://github.com/Project-MONAI/tutorials/tree/main/auto3dseg Slicer Extension: https://github.com/lassoan/SlicerMONAIAuto3DSeg

pathology model: https://github.com/knowledgevis/rms-infer-code-standalone

NA-MIC / ProjectWeek