Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images of 10 Cancer Types
This provides a valuable resource for cancer diagnosis and research, though it is incremental as it focuses on data generation rather than methodological breakthroughs.
The authors tackled the lack of large-scale, accurate, publicly accessible nucleus segmentation data in histopathology images by developing an analysis pipeline with quality control, resulting in a dataset of roughly 5 billion quality-controlled nuclei from 5,060 whole slide images across 10 cancer types.
The distribution and appearance of nuclei are essential markers for the diagnosis and study of cancer. Despite the importance of nuclear morphology, there is a lack of large scale, accurate, publicly accessible nucleus segmentation data. To address this, we developed an analysis pipeline that segments nuclei in whole slide tissue images from multiple cancer types with a quality control process. We have generated nucleus segmentation results in 5,060 Whole Slide Tissue images from 10 cancer types in The Cancer Genome Atlas. One key component of our work is that we carried out a multi-level quality control process (WSI-level and image patch-level), to evaluate the quality of our segmentation results. The image patch-level quality control used manual segmentation ground truth data from 1,356 sampled image patches. The datasets we publish in this work consist of roughly 5 billion quality controlled nuclei from more than 5,060 TCGA WSIs from 10 different TCGA cancer types and 1,356 manually segmented TCGA image patches from the same 10 cancer types plus additional 4 cancer types. Data is available at https://doi.org/10.7937/tcia.2019.4a4dkp9u