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Evaluation of imi-bigpicture/wsidicomizer as a tool for conversion into DICOM whole slide imaging format

Key Investigators

• Andrey Fedorov (BWH, USA)
• Daniela Schacherer (Fraunhofer MEVIS, Germany)
• David Clunie (Pixelmed Publishing, USA)

Project Description

DICOM standard is gaining acceptance in digital pathology imaging. Conversion of slide images into DICOM format can make the data more FAIR, improve quality and comprehensiveness of the associated metadata, and improve their interoperability with the commercial and open source tools implementing the standard.

DICOM format is used for slide microscopy images available in NCI Imaging Data Commons (IDC). Images submitted to IDC in the vendor-specific formats must be converted into DICOM representation, which is currently done using the PixelMed Toolkit based scripts available in https://github.com/ImagingDataCommons/idc-wsi-conversion.

Our goal is to migrate the DICOM WSI conversion to use community-supported open source tools. Based on our current assessment and experience, imi-bigpicture/wsidicomizer is the most promising tools available for this task. In this project we will work on evaluating this tool.

Objective

1. Assemble inventory of the publicly available test samples representative of the variety of data encountered by IDC and perhaps outside of IDC.
2. Document requirements for the conversion tool based on the needs of IDC.
3. Complete evaluation of wsidicomizer and document the results (in terms of the features and performance of the conversion process).
4. Document results and identified gaps to help with the next steps.

Approach and Plan

1. Select representative source images in the original format and the results of conversion to DICOM available in IDC (as converted using PixelMed Toolkit), as a reference. Assemble information about the characteristics of those samples in a document (vendor, compression, …). Include the accompanying tabulated metadata that is needed for converting each particulare sample.
2. Requirements: initialization of metadata, standard compliance of the result, transfer of ICC profile, acceptable performance …. (intentionally, DICOM-TIFF dual personality at this point is not a requirement)
3. Create a publicly available script/notebook that performs conversion.
4. Evaluate the results and summarize in a publicly available document.
5. Document any identified problems by opening issues in the wsidicomizer repo.

Progress and Next Steps

1. Describe specific steps you have actually done.

Illustrations

No response

Background and References

Background reading:

• Herrmann, M. D., Clunie, D. A., Fedorov, A., Doyle, S. W., Pieper, S., Klepeis, V., Le, L. P., Mutter, G. L., Milstone, D. S., Schultz, T. J., Kikinis, R., Kotecha, G. K., Hwang, D. H., Andriole, K. P., John Lafrate, A., Brink, J. A., Boland, G. W., Dreyer, K. J., Michalski, M., Golden, J. A., Louis, D. N. & Lennerz, J. K. Implementing the DICOM standard for digital pathology. J. Pathol. Inform. 9, 37 (2018). http://dx.doi.org/10.4103/jpi.jpi_42_18
• Clunie, D. A. DICOM format and protocol standardization-A core requirement for digital pathology success. Toxicol. Pathol. 49, 738–749 (2021). http://dx.doi.org/10.1177/0192623320965893

Other related materials:

• IDC Portal: https://portal.imaging.datacommons.cancer.gov/
• conversion_mirax_dicom (currently, private repo)
• Related earlier project from PW40: https://projectweek.na-mic.org/PW40_2024_GranCanaria/Projects/WsiDicomImprovementFromViewerToAnalysis/
• Code from Fabian Hörst experiments: https://github.com/TIO-IKIM/PathoPatcher/blob/main/pathopatch/patch_extraction/patch_extraction.py
• Test samples used by wsidicomizer: https://github.com/imi-bigpicture/wsidicomizer/blob/main/tests/download_test_images.py#L23-L60
• Google WSI transformation pipeline