Analysis Result Image segmentations produced by the AIMI Annotations initiative

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Details

Subject Count: 4226

Primary Site: Brain, Breast, Chest, Colon, Kidney, Liver, Lung, Prostate

Analysis Artifacts: SEG

Cancer Type(s): Breast Cancer, Clear Cell Carcinoma, Colorectal Cancer, Glioblastoma, Hepatocellular carcinoma, Kidney Chromophobe, Kidney Renal Clear Cell Carcinoma, Kidney Renal Papillary Cell Carcinoma, Liver Hepatocellular Carcinoma, Lung Adenocarcinoma, Lung Cancer, Lung Squamous Cell Carcinoma, Non-Cancer, Non-small Cell Lung Cancer, Prostate Cancer

DOIs

BAMF-AIMI-Annotations 10.5281/zenodo.8345959

Collections analyzed:

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Description

Many of the collections in IDC 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 nnU-Net 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 AI segmentations, manually corrected segmentations, and manual scores for the inspected IDC Collection images. Please see the BAMF-AIMI-Annotations wiki page to learn more about the images and to obtain any supporting metadata for this collection.