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NA-MIC Project Weeks

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Histology AI model annotations imported into IDC

Key Investigators

Presenter location: In-person

Project Description

This project focuses on importing whole slide image (WSI) histology images and trained deep learning models into the Imaging Data Commons for access by others. We have developed tissue-level segmentation models for detecting subtypes of rhabdomyosarcoma (RMS) in whole slides. Our project is releasing WSIs and the corresponding models trained on the slide images.

This project will test reading DICOM-WSI imagery (including compression) and focus on how to write out model segmentation results as DICOM-WSI annotations for upload to IDC. We also have classification and regression models, so we need to decide how to write non-imagery classification results as DICOM, as well.


Approach and Plan

Progress and Next Steps


Here is the model output drawn as an RGB pseudocolor image. Each tissue class determined by the model is given a different color: colorimage

Here is the stained pathology slide image and the model output written as a DICOM segmentation image and overlayed in the Slim viewer developed by the IDC program. The viewer is zoomed into the right part of the image:


After Project Week

We want to extend this algorithm to address segmentation images of the same extent, but different resolution than the original image. For this week, we kept the same resolution between the source image and the segmentation image generated by the model.

Background and References