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Localtab Group


Localtab
activetrue
titleData Access

Data Access

Click the  Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever . Click the Search button to open our Data Portal, where you can browse the data collection and/or download a subset of its contents.

Data TypeDownload all or Query/Filter
Images (DICOM, 5.5 GB)

 

Annotated Whole Slide Pathology Images (TIF, 21.3 GB)
Clinical data (XLSX)

Click the Versions tab for more info about data releases.


Localtab
titleDetailed Description

Detailed Description

Collection Statistics

 Radiology Image Statistics

Pathology Image Statistics

Modalities

CT, Histology compartments mapped on CT (DICOM)

Pathology

Number of Participants

6

6

Number of Studies

66

Number of Series

52N/A

Number of Images

11,210

25
Image Size (GB)5.521.3

Supporting Documentation

Within the directory CT_Segmentations_and_annotations/CT_Segmentations/<ptID>/ there are five directories:

  1. BloodVessels (derived from CT)
  2. Nodule (derived from CT)
  3. MappedFromHistologyBloodVessels
  4. MappedFromHistologyInvasion
  5. MappedFromHistologyLesion

The data set is fully described in the following publications:

Rusu et al. Co-registration of pre-operative CT with ex vivo surgically excised ground glass nodules to define spatial extent of invasive adenocarcinoma on in vivo imaging: a proof-of-concept study. European Radiology (2018); PMCID:PMC5630490 DOI:10.1007/s00330-017-4813-0

Histology Data Description

There is one folder for each patient, with the same folder name as the TCIA ID. Each folder with TCIA ID name contains 2 folders:

  • “images”
    • Contains scanned histology images (in tiff format) pertinent to the TCIA ID Patient, e.g. LungFCP-01-0001_b1.tiff, LungFCP-01-0001_b2.tiff, etc
  • “annotations”
    • Contains the pathologist’s annotations, stored as tiff file with the same image size as the scanned histology files, with 0 where there is not label, and 255 where a region was annotated. It is possible to have multiple annotations for each file, e.g. for file LungFCP-01-0001\images\LungFCP-01-0001_b1.tiff the following regions are available:
      • LungFCP-01-0001_b1_annotation_00_R000G255B000.tiff
      • LungFCP-01-0001_b1_annotation_00_R255G000B000.tiff

A part of the filenames (LungFCP-01-0001_b1_annotation_00_RxxxGyyyBzzz) indicate the type of annotation:

  • Adenocarcinoma in Situ : R000G000B255, R001G000B255, R002G000B255
  • Invasive Adenocarcinoma: R000G255B000, R001G255B000, R002G255B000, R003G255B000, R004G255B000, R005G255B000, R006G255B000
  • Invasive Adenocarcinoma + Adenocarcinoma in Situ: R255G000B000

If multiple files are available for the same type of annotation, e.g. Invasive Adenocarcinoma, it indicates that the pathologists has annotated multiple regions

Description of the content of “FinalPublishedResults” folder

The folder “FinalPublishedResults” contains one folder for each patient. Within each patient folder, there are 3 subfolders: “CT”, ”Histology”, & ”Results_XX”. When expanded each patient folder  appears as depicted below.

  • LungFCP-01-0001
    • CT
    • Histology
    • Results_XX
  • LungFCP-01-0002
  • LungFCP-01-0003
  • LungFCP-01-0004
  • LungFCP-01-0005
  • LungFCP-01-0006

The folder “CT” contains the 3D CT volume saved as one mha file, the segmentation of the lesion obtained by majority voting for the three radiologist segmentations, and the segmentation of the blood vessels.

The folder “histology” has a similar content as the raw histology data, but a lower resolution, with some additional processing, e.g. applied gross rotation and flipping to correct for artifacts related to the mounting on the glass slide, and with additional annotations beside the lesion and in situ disease, e.g. blood vessels.

The folder Results contain the outcome of the registration of the histopathology images and the CT. All scripts used for generating these results are available at https://github.com/mirabelarusu/RadPathFusionLung

Please refer to the following publication for the methodological details:

Rusu M., Rajiah P., Gilkeson R., Yang M., Donatelli C., Thawani R., Jacono F.J., Linden P.,  Madabushi A. (2017) Co-registration of pre-operative CT with ex vivo surgically excised ground glass nodules to define spatial extent of invasive adenocarcinoma on in vivo imaging: a proof-of-concept study. European Radiology 27:10, 4209:4217. DOI: https://doi.org/10.1007/s00330-017-4813-0


Localtab
titleCitations & Data Usage Policy

Citations & Data Usage Policy 

This collection is freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. See TCIA's Data Usage Policies and Restrictions for additional details. Questions may be directed to help@cancerimagingarchive.net.

Please be sure to include the following citations in your work if you use this data set:

Public collection license
Info
titleData Citation

Madabhushi, A., & Rusu, M. (2018). Fused Radiology-Pathology Lung Dataset. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2018.SMT36LPN


Info
titlePublication Citation

Rusu et al. Co-registration of pre-operative CT with ex vivo surgically excised ground glass nodules to define spatial extent of invasive adenocarcinoma on in vivo imaging: a proof-of-concept study. European Radiology (2018); PMCID:PMC5630490 DOI:10.1007/s00330-017-4813-0


Info
titleTCIA Citation

Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. (paper)

Other Publications Using This Data

TCIA maintains a list of publications that leverage our data. At this time we are not aware of any publications based on this data. If you have a publication you'd like to add, please contact the TCIA Helpdesk.


Localtab
titleVersions

Version 1 (Current) Updated 07-30-2018

Data TypeDownload all or Query/Filter
Images (DICOM,  5.5 GB)

 

(Requires the NBIA Data Retriever .)

Annotated Whole Slide Pathology Images (TIF, 21.3 GB)
Clinical data (XLSX)




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