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Pathology Extension for MHub IO Modules

Key Investigators

• Curtis Lisle (KnowledgeVis, LLC, USA)
• Leonard Nürnberg (MGB, Harvard, The Netherlands)

Project Description

Pathology (DICOM) images differ greatly from radiology images, e.g., contain multiple resolutions. The MHub core provides 18 IO Modules to import, convert, organize, imaging data. We want to extend this with additional IO Modules to extract a target resolution and to provide an alternative toolchain to generate DICOMSEG output files. The IO Modules will be made publicly available as an MHub.ai module extension.

Objective

1. A public pathology extension for MHub.ai
2. Adding the RMS model to MHub.ai utilizing the provided IO Modules as a PoC

Approach and Plan

1. Create a new pathology extension repository
2. Implement an extractor module
3. Implement a specific dicomseg conversion module (e.g., based on highdicom)
4. Implement the RMS model as PoC

Progress and Next Steps

1. Describe specific steps you have actually done.

Illustrations

Layer Extraction

Proposed Pipeline

RMS Model

Background and References

• MHub Documentation - Using Model Extensions
• MHub Documentation - Example Collection Repository
• Reproducible Radiology and Pathology Imaging Analysis Applications in MHub Slide 17-22
• MHub.ai