QIN-LungCT-Seg (QIN multi-site collection of Lung CT data with Nodule Segmentations) is a lung CT segmentation dataset from the TCIA database, containing CT imaging data of non-small cell lung cancer. These images are sourced from the RIDER Lung CT database, LIDC-IDRI database, and institutions such as Stanford University Medical Center and Moffitt Cancer Center, comprising data from 31 patients. Each tumor volume was segmented three times under different initial conditions. The dataset is designed to support comparisons of segmentation algorithms, determine acceptable ranges for volume and boundary variations, and develop algorithms for cancer biomarker discovery based on tumor characteristics.
This dataset serves as a high-value resource for cancer research, particularly in the development and validation of tumor segmentation algorithms. Additionally, the combined CT images and segmentation data provide researchers with a complete set of analysis-ready data, enabling the exploration of various cancer imaging processing and analysis techniques. Researchers can use this dataset to develop more advanced algorithms to aid in cancer diagnosis and treatment.
| Dimensions | Modality | Task Type | Anatomical Structures | Anatomical Area | Number of Categories | Data Volume | File Format |
|---|---|---|---|---|---|---|---|
| 3D | CT | Segmentation | Lung Cancer | Lung | 1 | 31 | .dcm |
| Dataset Statistics | spacing (mm) | size |
|---|---|---|
| min | (0.51, 0.51, 0.625) | (512, 512, 103) |
| median | (0.68, 0.68, 1.25) | (512, 512, 237) |
| max | (0.86, 0.86, 3.0) | (512, 512, 481) |
Number of 2D slices in the dataset: 6814.
| Metric | lung cancer |
|---|---|
| Case Count | 1 |
| Coverage | 100% |
| Min Volume (cm³) | 0.03 |
| Median Volume (cm³) | 4.06 |
| Max Volume (cm³) | 45.28 |
Red label is lung cancer.
QIN LUNG CT
│
├── QIN-LSC-0003
│ ├── 04-01-2015-1-NA-41946
│ ├── 08-06-2003-1-CT Thorax wContrast-41946
│ ├── 11-06-2014-1-NA-41946
├── QIN-LSC-00014
│ ├── ..
├── QIN-LSC-00028
│ ├── ..
├── ...
Jayashree Kalpathy-Cramer (Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA)
Binsheng Zhao (Department of Radiology, Columbia University Medical Center, New York, NY, USA)
Dmitry Goldgof (Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA)
Yuhua Gu (Departments of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA)
Xingwei Wang (Department of Radiology, Stanford University School of Medicine, James H. Clark Center S323 318 Campus Drive, Stanford, CA, 94305-5450, USA)
Hao Yang (Department of Radiology, Columbia University Medical Center, New York, NY, USA)
Yongqiang Tan (Department of Radiology, Columbia University Medical Center, New York, NY, USA)
Robert Gillies (Departments of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA)
Sandy Napel (Department of Radiology, Stanford University School of Medicine, James H. Clark Center S323 318 Campus Drive, Stanford, CA, 94305-5450, USA)
Official Website: https://www.cancerimagingarchive.net/analysis-result/qin-lungct-seg/
Download Link: https://www.cancerimagingarchive.net/analysis-result/qin-lungct-seg/
Article Address: https://link.springer.com/article/10.1007/s10278-016-9859-z
Publication Date: 2018-12
@article{kalpathy2016comparison,
title={A comparison of lung nodule segmentation algorithms: methods and results from a multi-institutional study},
author={Kalpathy-Cramer, Jayashree and Zhao, Binsheng and Goldgof, Dmitry and Gu, Yuhua and Wang, Xingwei and Yang, Hao and Tan, Yongqiang and Gillies, Robert and Napel, Sandy},
journal={Journal of digital imaging},
volume={29},
pages={476--487},
year={2016},
publisher={Springer}
}
Original introduction article is here.