Summary
This dataset contains standardized DICOM representation of the annotations and characterizations collected by the LIDC/IDRI initiative, originally stored in XML and available in the TCIA Data from The
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Lung Image Database Consortium (LIDC
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) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans (LIDC-IDRI) collection . Only the nodules that were deemed to be greater or equal to 3 mm in the largest planar dimensions have been annotated and characterized by the expert radiologists performing the annotations. Only those nodules are included in the present dataset.
Conversion was enabled by the pylidc library (https://pylidc.github.io/) (parsing of XML, volumetric reconstruction of the nodule annotations, clustering of the annotations belonging to the same nodule, calculation of the volume, surface area and largest diameter of the nodules) and the dcmqi library (https://github.com/qiicr/dcmqi) (storing of the annotations into DICOM Segmentation objects, and storing of the characterizations and measurements into DICOM Structured Reporting objects). The script used for the conversion is available at https://github.com/qiicr/lidc2dicom. The details on the process of the conversion and the usage of the resulting objects are available in the citation (see Citations & Data Usage Policy section).
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Description
This dataset contains DICOM-SEG (DSO) conversions of the Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection and Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-GBM collection analysis datasets, registered to the original spaces of the DICOM volumes from which they were derived. The MR volumes provided in the original segmentation datasets (T1 pre-contrast, T1 post-contrast, T2, FLAIR) are in NIfTI format, co-registered to an atlas space, and re-sampled to 1mm isotropic resolution. The original automatic and semi-automatic segmentations are also in the space and resolution of this atlas space. This dataset contains DICOM-SEG versions of the same segmentations, transformed back into the space of the DICOM patient datasets released in the original TCGA-GBM and TCGA-LGG datasets. For each patient in the segmentation dataset, each of the original NIfTI MR volumes has been registered and resampled back to their original patient space and resolution using 3DSlicer’s BRAINSFit module. The affine transformation files from these registrations are saved, and used to register and resample both the semi-automatic and automatic segmentations into the spaces of each original MR DICOM dataset. The resulting dataset contains four sets of registered segmentations for each original segmentation, as each segmentation has been registered to the unique spacing and resolution of the untransformed pre-contrast T1, post-contrast T1, T2, and FLAIR DICOM datasets. These resulting NIfTI segmentations are then converted into DICOM-SEG datasets using the software package dcmqi. DICOM-SEG metadata values specifying tissue type, algorithm properties, and study qualities are encoded in JSON objects, which are provided in this dataset. Affine transformations from the original NIfTI datasets to the TCGA DICOM datasets are also made available for download in ITK transform format.
Please also cite the following original datasets and manuscript when citing this dataset:
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Armato III, Samuel G., McLennan, Geoffrey, Bidaut, Luc, McNitt-Gray, Michael F., Meyer, Charles R., Reeves, Anthony P., … Clarke, Laurence P. (2015). Data From LIDC-IDRI. The Cancer Imaging Archive. http
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Download
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DICOM Segmentations, Registration Transformations, and DCMQI metadata
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