Image segmentations produced by BAMF under the AIMI Annotations initiative

The Imaging Data Commons (IDC)(https://imaging.datacommons.cancer.gov/) [1] connects researchers with publicly available cancer imaging data, often linked with other types of cancer data. Many of the collections have limited annotations due to the expense and effort required to create these manually. The increased capabilities of AI analysis of radiology images provide an opportunity to augment existing IDC collections with new annotation data. To further this goal, we trained several nnUNet [2] based models for a variety of radiology segmentation tasks from public datasets and used them to generate segmentations for IDC collections.

To validate the model's performance, roughly 10% of the AI predictions were assigned to a validation set. For this set, a board-certified radiologist graded the quality of AI predictions on a Likert scale. If they did not 'strongly agree' with the AI output, the reviewer corrected the segmentation. 

This record provides the AI segmentations, Manually corrected segmentations, and Manual scores for the inspected IDC Collection images.

Only 10% of the AI-derived annotations provided in this dataset are verified by expert radiologists . More details, on model training and annotations are provided within the associated manuscript to ensure transparency and reproducibility.

This work was done in two stages. Versions 1.x of this record were from the first stage. Versions 2.x added additional records. In the Version 1.x collections, a medical student (non-expert) reviewed all the AI predictions and rated them on a 5-point Likert Scale, for any AI predictions in the validation set that they did not 'strongly agree' with, the non-expert provided corrected segmentations. This non-expert was not utilized for the Version 2.x additional records.

Likert Score Definition:

Guidelines for reviewers to grade the quality of AI segmentations.

• 5 Strongly Agree - Use-as-is (i.e., clinically acceptable, and could be used for treatment without change)
• 4 Agree - Minor edits that are not necessary. Stylistic differences, but not clinically important. The current segmentation is acceptable
• 3 Neither agree nor disagree - Minor edits that are necessary. Minor edits are those that the review judges can be made in less time than starting from scratch or are expected to have minimal effect on treatment outcome
• 2 Disagree - Major edits. This category indicates that the necessary edit is required to ensure correctness, and sufficiently significant that user would prefer to start from the scratch
• 1 Strongly disagree - Unusable. This category indicates that the quality of the automatic annotations is so bad that they are unusable.

Zip File Folder Structure

Each zip file in the collection correlates to a specific segmentation task. The common folder structure is

• ai-segmentations-dcm This directory contains the AI model predictions in DICOM-SEG format for all analyzed IDC collection files
• qa-segmentations-dcm This directory contains manual corrected segmentation files, based on the AI prediction, in DICOM-SEG format. Only a fraction, ~10%, of the AI predictions were corrected. Corrections were performed by radiologist (rad*) and non-experts (ne*)
• qa-results.csv CSV file linking the study/series UIDs with the ai segmentation file, radiologist corrected segmentation file, radiologist ratings of AI performance.

qa-results.csv Columns

The qa-results.csv file contains metadata about the segmentations, their related IDC case image, as well as the Likert ratings and comments by the reviewers.

File Overview

brain-mr.zip

• Segment Description: brain tumor regions: necrosis, edema, enhancing
• IDC Collection: UPENN-GBM
• Links: model weights, github

breast-fdg-pet-ct.zip

• Segment Description: FDG-avid lesions in breast from FDG PET/CT scans QIN-Breast
• IDC Collection: QIN-Breast
• Links: model weights, github

breast-mr.zip

• Segment Description: Breast, Fibroglandular tissue, structural tumor
• IDC Collection: duke-breast-cancer-mri
• Links: model weights, github

kidney-ct.zip

• Segment Description: Kidney, Tumor, and Cysts from contrast enhanced CT scans
• IDS Collection: TCGA-KIRC, TCGA-KIRP, TCGA-KICH, CPTAC-CCRCC
• Links: model weights, github

liver-ct.zip

• Segment Description: Liver from CT scans
• IDC Collection: TCGA-LIHC
• Links: model weights, github

liver2-ct.zip

• Segment Description: Liver and Lesions from CT scans
• IDC Collection: HCC-TACE-SEG, COLORECTAL-LIVER-METASTASES
• Links: model weights, github

liver-mr.zip

• Segment Description: Liver from T1 MRI scans
• IDC Collection: TCGA-LIHC
• Links: model weights, github

lung-ct.zip

• Segment Description: Lung and Nodules (3mm-30mm) from CT scans
• IDC Collections:
  
  • Anti-PD-1-Lung
  • LUNG-PET-CT-Dx
  • NSCLC Radiogenomics
  • RIDER Lung PET-CT
  • TCGA-LUAD
  • TCGA-LUSC
• Links: model weights 1, model weights 2, github

lung2-ct.zip

• Improved model version
• Segment Description: Lung and Nodules (3mm-30mm) from CT scans
• IDC Collections:
  
  • QIN-LUNG-CT, SPIE-AAPM Lung CT Challenge
• Links: model weights, github

lung-fdg-pet-ct.zip

• Segment Description: Lungs and FDG-avid lesions in the lung from FDG PET/CT scans
• IDC Collections:
  • ACRIN-NSCLC-FDG-PET
  • Anti-PD-1-Lung
  • LUNG-PET-CT-Dx
  • NSCLC Radiogenomics
  • RIDER Lung PET-CT
  • TCGA-LUAD
  • TCGA-LUSC
• Links: model weights, github

prostate-mr.zip

• Segment Description: Prostate from T2 MRI scans
• IDC Collection: ProstateX, Prostate-MRI-US-Biopsy
• Links: model weights, github

Changelog

• 2.0.2 - Fix the brain-mr segmentations to be transformed correctly
• 2.0.1 - added AIMI 2024 radiologist comments to qa-results.csv
• 2.0.0 - added AIMI 2024 segmentations
• 1.X - AIMI 2023 segmentations and reviewer scores