{"id":48983,"date":"2024-09-04T14:08:57","date_gmt":"2024-09-04T19:08:57","guid":{"rendered":"https:\/\/www.cancerimagingarchive.net\/?post_type=tcia_collection&p=48983"},"modified":"2024-11-20T08:46:48","modified_gmt":"2024-11-20T14:46:48","slug":"brats-africa","status":"publish","type":"tcia_collection","link":"https:\/\/stage.cancerimagingarchive.net\/collection\/brats-africa\/","title":{"rendered":"BRATS-AFRICA"},"featured_media":0,"template":"","class_list":["post-48983","tcia_collection","type-tcia_collection","status-publish"],"cancer_types":["Brain Cancer"],"citations":[49495],"collection_doi":"10.7937\/v8h6-8x67","collection_download_info":"Some data in this collection contains images that could potentially be used to reconstruct a human face. To safeguard the privacy of participants, users must sign and submit a\u00a0TCIA Restricted License Agreement<\/a>\u00a0to\u00a0help@cancerimagingarchive.net<\/a>\u00a0before accessing the data.","collection_downloads":[49477,49479,49481],"versions":false,"additional_resources":"","cancer_locations":["Brain"],"collection_page_accessibility":"Public","publications_related":"","version_change_log_archived":"","collection_status":"Complete","publications_using":"TCIA maintains\u00a0a list of publications<\/a>\u00a0that leveraged this dataset. If you have a manuscript you\u2019d like to add please\u00a0contact TCIA\u2019s Helpdesk<\/a>.","related_analysis_results":false,"species":["Human"],"version_number":"1","collection_title":"Expanding the Brain Tumor Segmentation (BraTS) data to include African Populations","date_updated":"2024-09-04","related_collection":false,"subjects":"146","analysis_results":"","collection_short_title":"BraTS-Africa","data_types":["MR","Segmentation"],"version_change_log":"","collection_browse_title":"BraTS-Africa","detailed_description":"

Table: Data Collection and Sources (see BraTS-Africa_TCIA_datainfo.xlsx<\/a> )<\/h4>\r\n<\/colgroup>\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
\r\n

Imaging Center<\/strong><\/p>\r\n<\/td>\r\n

\r\n

Manufacturer<\/strong><\/p>\r\n<\/td>\r\n

\r\n

Model (1.5T MRI)<\/strong><\/p>\r\n<\/td>\r\n

\r\n

Resolution T1 (mm)<\/strong><\/p>\r\n<\/td>\r\n

\r\n

Resolution T2 (mm)<\/strong><\/p>\r\n<\/td>\r\n

\r\n

Resolution T2-FLAIR (mm)<\/strong><\/p>\r\n<\/td>\r\n<\/tr>\r\n

CRV<\/td>\r\nSiemens<\/td>\r\nMagnetom Essenza<\/td>\r\n1 x 1 x 5<\/td>\r\n1 x 1 x 5<\/td>\r\n1 x 1 x 5<\/td>\r\n<\/tr>\r\n
LASUTH<\/td>\r\nPhilips<\/td>\r\nAchieva<\/td>\r\n1 x 1 x 4.5<\/td>\r\n1 x 1 x 5<\/td>\r\n1 x 1 x 5<\/td>\r\n<\/tr>\r\n
LILY<\/td>\r\nGE<\/td>\r\nSIGNA Explorer<\/td>\r\n1 x 1 x 3<\/td>\r\n1 x 1 x 3<\/td>\r\n1 x 1 x 3<\/td>\r\n<\/tr>\r\n
NKDC<\/td>\r\nSiemens<\/td>\r\nMagnetom Essenza<\/td>\r\n1 x 1 x 5<\/td>\r\n1 x 1 x 5<\/td>\r\n1 x 1 x 5<\/td>\r\n<\/tr>\r\n
MEDHUB<\/td>\r\nGE<\/td>\r\nSIGNA Creator<\/td>\r\n1 x 1 x 4<\/td>\r\n1 x 1 x 4<\/td>\r\n1 x 1 x 4<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\nThe list of software tools used in image preprocessing are:<\/span>\r\n\r\n\r\n\r\n\r\n\r\n\r\n
Image preprocessing<\/strong><\/span><\/td>\r\nSoftware<\/strong><\/td>\r\nCitation #<\/strong><\/td>\r\n<\/tr>\r\n
-Dicom conversion to NIfTI<\/span>\r\n\r\n-Registration to SRI24<\/span>\r\n\r\n-Resampling to isotropic resolution (1mm^<\/span>3<\/span>)<\/span><\/td>\r\nCancer Imaging Phenomics Toolkit (CaPTk) (version 1.9.0 )<\/span><\/td>\r\n1<\/span><\/td>\r\n<\/tr>\r\n
-Skull stripping<\/span>\r\n\r\n-Intensity normalization<\/span>\r\n\r\n-pre-annotation to tumor subregions<\/span><\/td>\r\nnnUNet deep learning method<\/span><\/td>\r\n2<\/span><\/td>\r\n<\/tr>\r\n
-review and approval of annotation labels<\/span><\/td>\r\nITK-SNAP (version 4.0.0)<\/span><\/td>\r\n3<\/span><\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\nReference:<\/span>\r\n<\/span>[1] S. Pati et al., \u201cThe Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview,\u201d in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, A. Crimi and S. Bakas, Eds., in Lecture Notes in Computer Science. Cham: Springer International Publishing, 2020, pp. 380\u2013394. doi: 10.1007\/978-3-030-46643-5_38<\/a>.<\/span>\r\n<\/span>[2] S. Bakas et al., \u201cAdvancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features,\u201d Sci Data, vol. 4, no. 1, Art. no. 1, Sep. 2017, doi: 10.1038\/sdata.2017.117<\/a>.<\/span>\r\n<\/span>[3] P. A. Yushkevich, Y. Gao, and G. Gerig, \u201cITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images,\u201d in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Aug. 2016, pp. 3342\u20133345. doi: 10.1109\/EMBC.2016.7591443<\/a><\/span>","supporting_data":false,"collection_featured_image":{"ID":"9095","post_author":"29","post_date":"2023-09-14 01:07:08","post_date_gmt":"2023-09-14 06:07:08","post_content":"","post_title":"BRATS_banner_noCaption","post_excerpt":"","post_status":"inherit","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"brats_banner_nocaption","to_ping":"","pinged":"","post_modified":"2023-11-30 13:22:07","post_modified_gmt":"2023-11-30 19:22:07","post_content_filtered":"","post_parent":"45739","guid":"https:\/\/stage.cancerimagingarchive.net\/wp-content\/uploads\/BRATS_banner_noCaption.png","menu_order":"0","post_type":"attachment","post_mime_type":"image\/png","comment_count":"0","pod_item_id":"9095"},"collection_summary":"

The dataset is a collection of retrospective pre-operative brain magnetic resonance imaging (MRI) scans, clinically acquired from six diagnostic centers in Nigeria. The scans are from 146 patients who have brain MRIs indicating central nervous system neoplasms, diffuse glioma, low-grade glioma, or glioblastoma\/high-grade glioma. The brain scans were multiparametric MR images (mpMRI), specifically T1, T1 CE, T2, and T2 FLAIR,\u00a0 acquired on 1.5T MRI between January 2010 and December 2022.\u00a0<\/p>

Scans were obtained from different scanners using each center\u2019s acquisition protocol. Each scan was de-identified and de-faced to remove personal identifiers and presented in their original state with respect to resolution and orientation. To ensure uniformity across scans and modalities, a standardized pre-processing protocol was applied to adjust the image dimensions and voxel sizes. The scans were extracted from the PACs as DICOM files and converted to the Neuroimaging Informatics Technology Initiative (NlfTI) file format to facilitate computational analysis, following the well-accepted pre-processing protocol of the International Brain Tumour Segmentation (BraTS) challenge. All scans were subjected to sanity checks to confirm the presence of all required sequences. Specifically, all mpMRI volumes were reoriented to the left posterior-superior (LPS) coordinate system, and the T1 CE scan of each patient was rigidly (6 degrees of freedom) registered and resampled to an isotropic resolution of 1 mm3 based on a common anatomical atlas, namely SRI. The remaining scans (i.e., T1, T2, FLAIR) of each patient were then rigidly co-registered to this resampled T1 CE scan by first obtaining the rigid transformation matrix to T1 CE, then combining with the transformation matrix from T1 CE to the SRI atlas, and resampling. The N4 bias field correction was applied in all scans to correct for intensity non-uniformities caused by the inhomogeneity of the scanner's magnetic field during image acquisition to facilitate an improved registration of all scans to the common anatomical atlas. Brain extraction was also performed using a standard process for\u00a0 skull-stripping to remove all non-brain tissue (including neck, fat, eyeballs, and skull) from the image and create a brain mask to\u00a0 enable further computational analyses.\u00a0<\/p>

Inclusion Criteria<\/h4>

\u00a0All Brain MRI Scans of patients with clinical features of brain tumors from the study site acquired between January 2010 and December 2022, including, central nervous system (CNS) neoplasms, specifically diffuse glioma, or low-grade glioma (LGG) or glioblastoma\/high-grade glioma (GBM\/HGG).<\/p>

Exclusion Criteria<\/h4>

\u00a0Any brain image or scan that is not an MRI or acquired before January 2010 or after December 2022.<\/p>

Image Annotation<\/h4>

The expert-annotated tumor sub-regions for each of the 146 cases are provided along with a metadata (csv file) of study location, scanner type, where available.<\/p>

Benefit to Researchers<\/h4>

The contribution of BraTS-Africa dataset is two-fold: 1) its potential for use in research leading towards generalizable and inclusive diagnostic tools applicable across all settings including resource constrained environments, and 2) its ability to describe the peculiarities of neuroimaging in African settings.<\/p>","collection_acknowledgements":"

This dataset was curated with the support of the Lacuna Fund<\/a> for Health and Equity.<\/p>","collection_funding":"","hide_from_browse_table":"0","program":["Community"],"_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/48983","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections"}],"about":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/types\/tcia_collection"}],"version-history":[{"count":1,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/48983\/revisions"}],"predecessor-version":[{"id":48985,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/48983\/revisions\/48985"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=48983"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}