AI-Generated Annotations Dataset for Diverse Cancer Radiology Collections in NCI Image Data Commons

TL;DR

This paper presents a large dataset of AI-generated and radiologist-verified annotations for diverse cancer radiology collections in the NCI Image Data Commons, enhancing the availability of high-quality labeled medical images for research.

Contribution

It introduces a comprehensive, publicly accessible dataset of AI-generated and validated annotations across multiple imaging modalities and cancer types, improving annotation coverage in IDC collections.

Findings

AI-generated annotations cover 11 collections with diverse modalities.

Radiologist review improves annotation accuracy and quality.

All data is standardized in DICOM format for easy integration.

Abstract

The National Cancer Institute (NCI) Image Data Commons (IDC) offers publicly available cancer radiology collections for cloud computing, crucial for developing advanced imaging tools and algorithms. Despite their potential, these collections are minimally annotated; only 4% of DICOM studies in collections considered in the project had existing segmentation annotations. This project increases the quantity of segmentations in various IDC collections. We produced high-quality, AI-generated imaging annotations dataset of tissues, organs, and/or cancers for 11 distinct IDC image collections. These collections contain images from a variety of modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). The collections cover various body parts, such as the chest, breast, kidneys, prostate, and liver. A portion of the AI annotations were reviewed and corrected by a radiologist to assess the performance of the AI models. Both the AI's and the radiologist's annotations were encoded in conformance to the Digital Imaging and Communications in Medicine (DICOM) standard, allowing for seamless integration into the IDC collections as third-party analysis collections. All the models, images and annotations are publicly accessible.