DOI: 10.5281/zenodo.10009368
title: Image segmentations produced by 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 provides 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 models performance, roughly 10% of the predictions were manually reviewed and corrected by both a board certified radiologist and a medical student (non-expert). Additionally, this non-expert looked at all the ai predictions and rated them on a 5 point Likert scale .
This record provides the AI segmentations, Manually corrected segmentations, and Manual scores for the inspected IDC Collection images.
File Overview
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
kidney-ct.zip

Segment Description: Kidney, Tumor, and Cysts from contrast enhanced CT scans
IDS Collection: TCGA-KIRC
Links: model weights, github
liver-ct.zip

Segment Description: Liver from CT scans
IDC Collection: TCGA-LIHC
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:

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 1, model weights 2, 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
Links: model weights, github
 
Likert Score Definition:

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.