DOI: 10.5281/zenodo.13175280
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 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.

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 2.x additions, the Likert scores were not reported for all ai predictions, only the ~10% reviewed by radiologists.

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


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.


 

Changelog

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