Project-MONAI / Project-MONAI/MONAI
port tcia-related code to tcia_utils
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- Python
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Description
Is your feature request related to a problem? Please describe.
It would be excellent if we could improve users' ability to query/download datasets from The Cancer Imaging Archive and load them in MONAI. Currently there are a few old examples of doing this in MONAI: https://github.com/search?q=org%3AProject-MONAI%20tcia&type=code. However, there is now a "tcia_utils" PyPI at https://pypi.org/project/tcia-utils/ and many examples of using it at https://github.com/kirbyju/TCIA_Notebooks that are being updated regularly.
Describe the solution you'd like
It would probably make sense to leverage port any TCIA-related code that lives in the MONAI repo over to https://github.com/kirbyju/tcia_utils and investigate whether there is anything else should be implemented in tcia_utils to make it easy to prep data for MONAI.
https://github.com/kirbyju/TCIA_Notebooks/blob/main/TCIA_REST_API_Downloads.ipynb provides several simple examples showing different use cases for downloading DICOM radiology data.
I think it would also be valuable to ensure that the DICOM SEG and RTSTRUCT segmentation data on TCIA can be easily loaded in MONAI. https://github.com/kirbyju/TCIA_Notebooks/blob/main/TCIA_Segmentations.ipynb has examples related to this.
I am also working on a new module that will be useful for using our newest API to extract supporting data that can be used for classification tasks (e.g. clinical demographics/outcomes, genomic and proteomic subtypes) and would love your input on how to make that most useful.
Additional context
This was discussed in the 1/26/24 MONAI developers meeting and I'm submitting this issue at the suggestion of @aylward and @ericspod.
Contributor guide
First steps
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Research direction
Start by locating the existing TCIA-related examples and code in the MONAI repository, then review the linked tcia_utils project and TCIA notebooks. Determine which functionality should move or be added, including DICOM SEG, RTSTRUCT, and supporting clinical or genomic data workflows. Done means the responsibilities and implementation scope are agreed with the relevant projects.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100