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.
List of all tasks and IDC collections analyzed.
File
Segmentation Task
IDC Collections
LInks
breast-fdg-pet-ct.zip
FDG-avid lesions in breast from FDG PET/CT scans
QIN-Breast
model weights
github
kidney-ct.zip
Kidney, Tumor, and Cysts from contrast enhanced CT scans
TCGA-KIRC
model weights
github
liver-ct.zip
Liver from CT scans
TCGA-LIHC
model weights
github
liver-mr.zip
Liver from T1 MRI scans
TCGA-LIHC
model weights
github
lung-ct.zip
Lung and Nodules (3mm-30mm) from CT scans
ACRIN-NSCLC-FDG-PET
Anti-PD-1-Lung
LUNG-PET-CT-Dx
NSCLC Radiogenomics
RIDER Lung PET-CT
TCGA-LUAD
TCGA-LUSC
model weights 1
model weights 2
github
lung-fdg-pet-ct.zip
Lungs and FDG-avid lesions in the lung from FDG PET/CT scans
ACRIN-NSCLC-FDG-PET
Anti-PD-1-Lung
LUNG-PET-CT-Dx
NSCLC Radiogenomics
RIDER Lung PET-CT
TCGA-LUAD
TCGA-LUSC
model weights
github
prostate-mr.zip
Prostate from T2 MRI scans
ProstateX
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.
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