DOI: 10.5281/zenodo.13244892
title: Image segmentations produced by BAMF under the AIMI Annotations initiative

description: 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.








Column



Description





Collection



The name of the IDC collection for this case





PatientID



PatientID in DICOM metadata of scan. Also called Case ID in the IDC





StudyInstanceUID



StudyInstanceUID in the DICOM metadata of the scan





SeriesInstanceUID



SeriesInstanceUID in the DICOM metadata of the scan





Validation



true/false if this scan was manually reviewed





Reviewer



Coded ID of the reviewer. Radiologist IDs start with ‘rad’ non-expect IDs start with ‘ne’





AimiProjectYear



2023 or 2024, This work was split over two years. The main methodology difference between the two is that in 2023, a non-expert also reviewed the AI output, but a non-expert was not utilized in 2024.





AISegmentation



The filename of the AI prediction file in DICOM-seg format. This file is in the ai-segmentations-dcm folder.





CorrectedSegmentation



The filename of the reviewer-corrected prediction file in DICOM-seg format. This file is in the qa-segmentations-dcm folder. If the reviewer strongly agreed with the AI for all segments, they did not provide any correction file.





Was the AI predicted ROIs accurate?



This column appears one for each segment in the task for images from AimiProjectYear 2023. The reviewer rates segmentation quality on a Likert scale. In tasks that have multiple labels in the output, there is only one rating to cover them all.





Was the AI predicted {SEGMENT_NAME} label accurate?

 



This column appears one for each segment in the task for images from AimiProjectYear 2024. The reviewer rates each segment for its quality on a Likert scale.





Do you have any comments about the AI predicted ROIs?

 



Open ended question for the reviewer





Do you have any comments about the findings from the study scans?



Open ended question for the reviewer






 

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