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Cancer02:18

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Cancers arise due to mutations in genes involved in the regulation of cell division, which leads to unrestricted cell proliferation. Modern science and medicine have made great strides in the understanding and treatment of cancer, including eradicating cancer in some patients. However, there is still no cure for cancer. This is largely due to the fact that cancer is a large group of many diseases.
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AI-Generated Annotations Dataset for Diverse Cancer Radiology Collections in NCI Image Data Commons.

Gowtham Krishnan Murugesan1, Diana McCrumb2, Mariam Aboian3

  • 1BAMF Health, Grand Rapids, MI, USA. gowtham.murugesan@bamfhealth.com.

Scientific Data
|October 23, 2024
PubMed
Summary
This summary is machine-generated.

This project generated AI-powered imaging annotations for cancer research collections, significantly increasing available segmentation data. These enhanced datasets support the development of advanced medical imaging tools and algorithms.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Cancer Research Informatics

Background:

  • The National Cancer Institute (NCI) Image Data Commons (IDC) provides valuable public cancer radiology data.
  • Existing segmentation annotations in IDC collections are scarce, limiting advanced AI development.
  • A significant gap exists in annotated medical imaging datasets for cancer research.

Purpose of the Study:

  • To augment existing National Cancer Institute (NCI) Image Data Commons (IDC) collections with high-quality segmentation annotations.
  • To enhance the utility of public cancer radiology datasets for AI-driven research.
  • To create a comprehensive, AI-generated dataset for medical imaging analysis.

Main Methods:

  • Utilized artificial intelligence (AI) to generate segmentation annotations for tissues, organs, and cancers across 11 IDC collections.
  • Incorporated diverse imaging modalities including computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET).
  • Ensured annotations conform to the Digital Imaging and Communications in Medicine (DICOM) standard for seamless integration.

Main Results:

  • Produced a substantial dataset of AI-generated annotations for 11 distinct IDC image collections.
  • Covered various anatomical regions such as the chest, breast, kidneys, prostate, and liver.
  • A subset of AI annotations were validated and corrected by a radiologist to assess AI model performance.

Conclusions:

  • The AI-generated annotations significantly increase the quantity and quality of segmentations in public cancer imaging datasets.
  • These enhanced DICOM-compliant datasets are readily integrable into the NCI IDC as third-party analysis collections.
  • The publicly accessible models, images, and annotations will accelerate the development of novel cancer imaging tools and algorithms.