Published November 7, 2023 | Version 1.7
Dataset Open

Image segmentations produced by BAMF under the AIMI Annotations initiative


The Imaging Data Commons (IDC)( [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

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

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

  • 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
  • Links: model weights 1, model weights 2, github

  • Segment Description: Lungs and FDG-avid lesions in the lung from FDG PET/CT scans
  • IDC Collections:
    • Anti-PD-1-Lung
    • LUNG-PET-CT-Dx
    • NSCLC Radiogenomics
    • RIDER Lung PET-CT
  • 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.



Files (99.4 MB)

Name Size Download all
805.1 kB Preview Download
7.6 MB Preview Download
4.7 MB Preview Download
1.7 MB Preview Download
72.7 MB Preview Download
9.7 MB Preview Download
2.1 MB Preview Download

Additional details

Related works


AIMI Annotations 75N91019D00024
National Cancer Institute


  • [1] Fedorov A, Longabaugh WJ, Pot D, Clunie DA, Pieper S, Aerts HJ, Homeyer A, Lewis R, Akbarzadeh A, Bontempi D, Clifford W. NCI imaging data commons. Cancer research. 2021 Aug 8;81(16):4188.
  • [2] Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.