Chest CT Segmentation Dataset - 1000 studies

The Chest CT Segmentation Dataset is a comprehensive collection of over 1,000 studies designed to support research and advancements in medical imaging. This dataset includes detailed CT scans highlighting 7 pathologies across 8 anatomical regions, offering an invaluable resource for lung segmentation, disease detection, and computer-aided diagnosis.

Key Features

• Extensive Data:

  • Covers 7 major pathologies and 8 anatomical regions to ensure wide applicability in research and clinical practice.

• File Format:

  • Data is provided in NII (Neuroimaging Informatics Technology Initiative) format, which includes volumetric imaging data and corresponding segmentation masks for each study.

• Applications:

  • Medical Research: Facilitate studies on imaging data to improve early disease detection methods.
  • AI in Healthcare: Train machine learning models for lung segmentation and automated diagnostic tools.
  • Clinical Practice: Enhance computer-aided screening techniques for better patient outcomes.

Dataset Specifications

• Volume and Masks:
  • Each study includes volumetric CT data and accurately labeled segmentation masks, enabling precise analysis and segmentation tasks.
• Pathology and Anatomy Coverage:
  • Comprehensive representation of both anatomical regions and pathological conditions ensures the dataset’s relevance for a variety of medical imaging projects.

Use Case Scenarios

1. Lung Segmentation Models: Support the development of accurate segmentation models for clinical and research purposes.
2. Disease Detection: Aid in training AI systems to identify and classify pathologies from CT scan data.
3. Medical Education: Provide a rich dataset for teaching imaging techniques and understanding lung and chest pathology.

This dataset is sourced from Kaggle.