skip to main content
Idioma:
Primo Search
Clear Search Box
Search in: Busca Geral
Busca Avançada
Busca por Índices

TIGER training dataset (ROI-level annotations of WSIROIS subset)

Van Rijthoven, Mart ; Aswolinskiy, Witali ; Tessier, Leslie ; Balkenhol, Maschenka ; Bogaerts, Joep ; Van Der Laak, Jeroen ; Salgado, Roberto ; Ciompi, Francesco

Zenodo 2022

Texto completo disponível

Citações Citado por
  • Título:
    TIGER training dataset (ROI-level annotations of WSIROIS subset)
  • Autor: Van Rijthoven, Mart ; Aswolinskiy, Witali ; Tessier, Leslie ; Balkenhol, Maschenka ; Bogaerts, Joep ; Van Der Laak, Jeroen ; Salgado, Roberto ; Ciompi, Francesco
  • Notas: RelationTypeNote: HasVersion -- 10.5281/zenodo.6014422
    10.5281/zenodo.6014422
  • Descrição: This dataset contains data and ROI-level annotations of the WSIROIS subset of the TIGER training dataset, released in conjunction with the TIGER challenge. Note that the WSIROIS dataset with whole-slide image-level annotations can be downloaded via the Data section of the TIGER challenge, together with the two additional subsets released with the challenge, namely the WSIBULK and the WSITILS subsets. The data is derived from digital pathology images of Her2 positive (Her2+) and Triple Negative (TNBC) breast cancer whole-slide images, together with manual annotations. Data comes from multiple sources. A subset of Her2+ and TNBC cases is provided by the Radboud University Medical Center (RUMC) (Nijmegen, Netherlands). A subset of Her2+ and TNBC cases is provided by the Jules Bordet Institut (JB) (Bruxelles, Belgium). A third subset of TNBC cases only is derived from the TCGA-BRCA archive obtained from the Genomic Data Commons Data Portal. This dataset of ROI-level annotation of WSIROIS is released in a format that is fully compatible with segmentation and detection pipelines used in the computer vision community. For this reason, we release regions of interest and manual annotations in PNG format and cell locations as bounding boxes in COCO format. In this way, we hope to make TIGER accessible to people that do not have experience with whole-slide images but still want to participate and contribute to this project. In this set, we release regions of interest from n=195 whole-slide images of breast cancer, both (core-needle) biopsies and surgical resections, with regions of interest (ROI) selected and manually annotated. All data (both images and manual annotations) are released at 0.5 um/px magnification. This dataset contains images and annotations from multiple sources: TCGA: regions of interest cropped from n=151 WSIs of TNBC cases from the TGCA-BRCA archive (the original slides can also be downloaded from the GDC Data Portal). Annotations are extracted and adapted from the publicly available BCSS and NuCLS datasets. RUMC: regions of interest cropped from n=26 WSIs of TNBC and Her2+ cases from Radboud University Medical Center (Netherlands). Annotations were made by a panel of board-certified breast pathologists. JB: regions of interest cropped from n=18 WSIs of TNBC and Her2+ cases from Jules Bordet Institute (Belgium). Annotations were made by a panel of board-certified breast pathologists. In this dataset, we release ROI-level annotations of both tissue compartments and cells. ROI images are released in PNG format; cell annotations are released as bounding boxes in the standard COCO format for object detection; tissue compartment annotations are released as PNG images containing pixel-wise class labels. In each image file, the coordinates of the region of interest in the WSI are indicated in the filename as imagefilename_[x1,y1,x2,y2].png, where (x1,y1) are the coordinates of the top-left corner and (x2,y2) are the coordinates of the bottom-right corner of each ROI. Check the Data section of the TIGER challenge for additional information about this dataset.
  • Editor: Zenodo
  • Data de criação/publicação: 2022
  • Idioma: Inglês
Links
Voltar para lista de resultados

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.