All NIfTI files have been compressed for convenience (.nii.gz)

Note: These segmentations use the RPI orientation, but the original DICOM files are oriented using the RAI convention.  As a result, some tools such as ITK-SNAP will not render the segmentations in the correct orientation when visualized.  The authors of these data suggest using software like Mango (http://ric.uthscsa.edu/mango/) or to convert to DICOM files to NIfTI with software like dcm2niix (https://github.com/rordenlab/dcm2niix) to address this issue.

We would like to acknowledge the individuals and institutions that have provided data for this collection:

• University of Texas M.D. Anderson Cancer Center,  Houston, TX,  USA – Special thanks to Kendall Kiser, MS Biomedical Informatics, from the Department of Radiation Oncology.
• The University of Texas Health Science Center School of Biomedical Informatics,  Houston, TX,  USA
• John P. and Kathrine G. McGovern Medical School, Houston, TX. Department of Diagnostic and Interventional Imaging.

Publications by the Dataset Authors

The authors recommended the following as the best source of additional information about this dataset:

Research Community Publications

TCIA maintains a list of publications that leveraged this dataset. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.

Version 2: Updated 2020/06/26

Version 2 changes:

• The dataset is now named “PleThora” for “Pleural effusion and thoracic cavity segmentations in diseased lungs.”
• All NIfTI files have been compressed for convenience (.nii à .nii.gz)
• All thoracic cavity primary reviewer segmentations have been renamed from “lungMask_edit.nii” to “[CaseID]_thor_cav_primary_reviewer.nii.gz” to more specifically identify each file’s contents and avoid confusion.
• Eighty-six thoracic cavity secondary reviewer segmentations have been added. These are named “[CaseID]_thor_cav_secondary_reviewer.nii.gz.”
• Interobserver variability analysis between primary and secondary reviewer thoracic cavity segmentations revealed four cases in which interobserver agreement was anomalously lower than all other cases. These cases were manually re-reviewed by another physician. In three cases (LUNG1-026, LUNG1-157, and LUNG1-354) it was deemed that the secondary reviewer’s segmentation excluded structures that should have been included. These were corrected. In one case (LUNG-088) it was determined that the primary reviewer segmentation included a large (400 cm3) nodal conglomerate. Our original thoracic cavity segmentation definition did not intend to include nodal conglomerates, so for consistency’s sake we corrected the primary reviewer segmentation accordingly. However, the segmentation with the nodal conglomerate is still valuable, so we provide it as well and name it “LUNG1-088_thor_cav_primary_reviewer_with_nodal_conglomerate.nii”
• We manually reviewed the pleural effusion segmentations of the primary physician reviewer and determined that in many cases the reviewer had not been sufficiently careful. Therefore, all 78 primary reviewer segmentations were re-reviewed by another physician and corrected as necessary. They are now re-submitted as “[CaseID]_effusion_first_reviewer.nii.gz”
• Seventy-eight pleural effusion secondary reviewer segmentations have been added. These are named “[CaseID]_effusion_second_reviewer.nii.gz.”
• Fifteen pleural effusion tertiary reviewer segmentations have been added. These are named “[CaseID]_effusion_third_reviewer.nii.gz.”
• We add two documents that describe baseline performances for 2D and 3D U-Net segmentation algorithms and define a reproducible train/test split.
• Data Dictionary: we provide a data dictionary to describe the meanings of column names in the “Thorax and Pleural Effusion Segmentation Metadata” spreadsheet.

Version 1: Updated 2020/04/03