Whole-Slide Images and Patches of Clear Cell Renal Cell Carcinoma Tissue Sections Counterstained with Hoechst 33342, CD3, and CD8 Using Multiple Immunofluorescence

Georg Wölflein (), In Hwa Um, David J. Harrison and Ognjen Arandjelović
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Georg Wölflein: School of Computer Science, University of St Andrews, North Haugh, St Andrews KY16 9SX, Scotland, UK
In Hwa Um: School of Medicine, University of St Andrews, North Haugh, St Andrews KY16 9TF, Scotland, UK
David J. Harrison: School of Medicine, University of St Andrews, North Haugh, St Andrews KY16 9TF, Scotland, UK
Ognjen Arandjelović: School of Computer Science, University of St Andrews, North Haugh, St Andrews KY16 9SX, Scotland, UK

Data, 2023, vol. 8, issue 2, 1-9

Abstract: In recent years, there has been an increased effort to digitise whole-slide images of cancer tissue. This effort has opened up a range of new avenues for the application of deep learning in oncology. One such avenue is virtual staining, where a deep learning model is tasked with reproducing the appearance of stained tissue sections, conditioned on a different, often times less expensive, input stain. However, data to train such models in a supervised manner where the input and output stains are aligned on the same tissue sections are scarce. In this work, we introduce a dataset of ten whole-slide images of clear cell renal cell carcinoma tissue sections counterstained with Hoechst 33342, CD3, and CD8 using multiple immunofluorescence. We also provide a set of over 600,000 patches of size 256 × 256 pixels extracted from these images together with cell segmentation masks in a format amenable to training deep learning models. It is our hope that this dataset will be used to further the development of deep learning methods for digital pathology by serving as a dataset for comparing and benchmarking virtual staining models.

Keywords: cancer; digital pathology; machine learning; deep learning; computer vision; virtual staining; segmentation (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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