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1. When Browsing Collections
   1. You can look for SEG / RTSTRUCT in the modality column to determine where DICOM segmentations or contours are available. 
   2. You can also filter for "Image Analyses" in the supporting data column.  If a collection says "Image Analyses" but does not include SEG or RTSTRUCT in the modality this is typically because the analysis was in some other format.  This could be segmentation data in NIFTI/NRRD/MHA formats, but it might also represent some other kind of analysis such as image classification.
2. When Browsing Analysis Results derived from TCIA collections, simply use the filter above the table to search for "segmentations" which will find any instance of this in the Analysis Artifacts column.
3. We also have a Jupyter notebook that shows how to use Python to search for and visualize segmentation data via our REST APIs 

Image classification

TCIA includes a wealth of non-image data that could be utilized for image classification purposes.  

1. Image Analyses
   1. You can also filter the table for "Image Analyses" on the Browse Collections page to find datasets with this type of information.  Image Analyses could include expert-derived image annotations (e.g. Where is the tumor located? What is the shape of the tumor?) or quantitative imaging features (e.g. What is the tumor volume? What is the texture of the tumor?). 
   2. The Browse Analysis Results page contains similar types of analysis data that were published by researchers who analyzed TCIA collections.
2. Clinical data (e.g. outcomes, stage) - Filter the table for "clinical" on the Browse Collections page to find datasets with this type of information
3. Distinguishing between cancer types (e.g. low grade vs high grade gliomas) - Cancer Type is one of the columns on the Browse Collections, making it easy to filter or search for datasets based on this criteria.
4. Genomic/Proteomic subtypes - Filter the table for "genomics" or "proteomics" on the Browse Collections page to find datasets with this type of information.  In most cases you will need to retrieve specific details about the patients' genomic/proteomic from external databases such as NCI's Genomic Data Commons or Proteomic Data Commons.  Please note these websites are not supported by TCIA staff, but we do coordinate with the teams that operate these archives to ensure common patient identifiers are used which enable you to link these data to TCIA images.

Interactive Python notebooks and tcia_utils package

There are a series of notebooks which demonstrate how to access and work with TCIA datasets using Python and our REST APIs. Most of them heavily leverage functionality from tcia_utils, which is a Python package that aims to provide functions to make it easier to work with TCIA datasets.

Guidance on sharing datasets related to Machine Learning or Artificial Intelligence studies on TCIA

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Third party tips and tutorials for applying deep learning to medical imaging data

1. RSNA Deep Learning Lab courses
   1. https://github.com
   /kirbyju/TCIA_Notebooks/blob/main/README.md - Tutorials for working with TCIA data in Jupyter Notebooks
   1. /RSNA/AI-Deep-Learning-Lab-2022
   2. https://github.com/RSNA/AI-Deep-Learning-Lab-2021
   3. https://github.com/RSNA/AI-Deep-Learning-Lab (2019)
2. https://github.com/RSNA/MagiciansCorner - Notebooks, datasets, other content for the Radiology:AI series known as Magicians Corner by Brad Erickson
3. http://modelhub.ai/ - a repository of self-contained deep learning models pretrained for a wide variety of applications which includes many models trained with TCIA datasets along with example notebooks
4. https://www.youtube.com/watch?v=-XUKq3B4sdw - how a radiologist interprets lung CTs
5. https://www.kaggle.com/gzuidhof/full-preprocessing-tutorial - how to pre-process images for deep learning
6. https://theaisummer.com/medical-image-coordinates/ - DICOM deep learning for medical imaging novices
7. https://developer.nvidia.com/clara-medical-imaging - NVIDIA package for simplifying deep learning tasks in medical imaging
8. https://forums.fast.ai/t/fastai-v2-has-a-medical-imaging-submodule/56117 - FastAI package for simplifying deep learning in medical imaging
9. "TCIA as a Centralized Data Resource for Development of AI" from RSNA 2019
10. https://www.kaggle.com/marcovasquez/basic-eda-data-visualization - RSNA intracranial hemorrhaging guide  
11. https://github.com/RSNA/AI-Deep-Learning-Lab - RSNA 2019 deep learning course
12. https://github.com/RSNA/MagiciansCorner - Notebooks, datasets, other content for the Radiology:AI series known as Magicians Corner by Brad Erickson