Whole-Slide Images and Patches of Clear Cell Renal Cell Carcinoma Tissue Sections Counterstained with Hoechst 33342, CD3, and CD8 Using Multiple Immunofluorescence
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 appe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Data |
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| Online Access: | https://www.mdpi.com/2306-5729/8/2/40 |
| _version_ | 1797621481039986688 |
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| author | Georg Wölflein In Hwa Um David J. Harrison Ognjen Arandjelović |
| author_facet | Georg Wölflein In Hwa Um David J. Harrison Ognjen Arandjelović |
| author_sort | Georg Wölflein |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T08:57:34Z |
| format | Article |
| id | doaj.art-0c9b20e148b14e4abceb4b1eb91af3d0 |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-03-11T08:57:34Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-0c9b20e148b14e4abceb4b1eb91af3d02023-11-16T19:58:56ZengMDPI AGData2306-57292023-02-01824010.3390/data8020040Whole-Slide Images and Patches of Clear Cell Renal Cell Carcinoma Tissue Sections Counterstained with Hoechst 33342, CD3, and CD8 Using Multiple ImmunofluorescenceGeorg Wölflein0In Hwa Um1David J. Harrison2Ognjen Arandjelović3School of Computer Science, University of St Andrews, North Haugh, St Andrews KY16 9SX, Scotland, UKSchool of Medicine, University of St Andrews, North Haugh, St Andrews KY16 9TF, Scotland, UKSchool of Medicine, University of St Andrews, North Haugh, St Andrews KY16 9TF, Scotland, UKSchool of Computer Science, University of St Andrews, North Haugh, St Andrews KY16 9SX, Scotland, UKIn 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.https://www.mdpi.com/2306-5729/8/2/40cancerdigital pathologymachine learningdeep learningcomputer visionvirtual staining |
| spellingShingle | Georg Wölflein In Hwa Um David J. Harrison Ognjen Arandjelović Whole-Slide Images and Patches of Clear Cell Renal Cell Carcinoma Tissue Sections Counterstained with Hoechst 33342, CD3, and CD8 Using Multiple Immunofluorescence Data cancer digital pathology machine learning deep learning computer vision virtual staining |
| title | Whole-Slide Images and Patches of Clear Cell Renal Cell Carcinoma Tissue Sections Counterstained with Hoechst 33342, CD3, and CD8 Using Multiple Immunofluorescence |
| title_full | Whole-Slide Images and Patches of Clear Cell Renal Cell Carcinoma Tissue Sections Counterstained with Hoechst 33342, CD3, and CD8 Using Multiple Immunofluorescence |
| title_fullStr | Whole-Slide Images and Patches of Clear Cell Renal Cell Carcinoma Tissue Sections Counterstained with Hoechst 33342, CD3, and CD8 Using Multiple Immunofluorescence |
| title_full_unstemmed | Whole-Slide Images and Patches of Clear Cell Renal Cell Carcinoma Tissue Sections Counterstained with Hoechst 33342, CD3, and CD8 Using Multiple Immunofluorescence |
| title_short | Whole-Slide Images and Patches of Clear Cell Renal Cell Carcinoma Tissue Sections Counterstained with Hoechst 33342, CD3, and CD8 Using Multiple Immunofluorescence |
| title_sort | whole slide images and patches of clear cell renal cell carcinoma tissue sections counterstained with hoechst 33342 cd3 and cd8 using multiple immunofluorescence |
| topic | cancer digital pathology machine learning deep learning computer vision virtual staining |
| url | https://www.mdpi.com/2306-5729/8/2/40 |
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