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Part 1: Document Description
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Citation |
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Title: |
Normalization of HE-Stained Histological Images using Cycle Consistent Generative Adversarial Networks [Dataset] |
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Identification Number: |
doi:10.11588/data/8LKEZF |
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Distributor: |
heiDATA |
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Date of Distribution: |
2021-07-27 |
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Version: |
1 |
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Bibliographic Citation: |
Runz, Marlen; Weis, Cleo-Aron, 2021, "Normalization of HE-Stained Histological Images using Cycle Consistent Generative Adversarial Networks [Dataset]", https://doi.org/10.11588/data/8LKEZF, heiDATA, V1 |
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Citation |
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Title: |
Normalization of HE-Stained Histological Images using Cycle Consistent Generative Adversarial Networks [Dataset] |
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Identification Number: |
doi:10.11588/data/8LKEZF |
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Authoring Entity: |
Runz, Marlen (Institute of Pathology Mannheim, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany) |
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Weis, Cleo-Aron (Institute of Pathology Mannheim, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany) |
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Distributor: |
heiDATA |
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Access Authority: |
Runz, Marlen |
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Holdings Information: |
https://doi.org/10.11588/data/8LKEZF |
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Study Scope |
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Keywords: |
Computer and Information Science, Medicine, Health and Life Sciences, Stain Normalization |
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Abstract: |
<p> Here we provide the data sets supporting the experiments in our publication <i>Normalization of HE-Stained Histological Images using Cycle Consistent Generative Adversarial Networks</i>, which were collected at the Institute of Pathology, Medical Faculty Mannheim, Heidelberg University. </p> <p>The HE-Staining Variation (HEV) data set offers serial sections of a follicular thyroid carcinoma, stained with different HE-staining protocols (including name of <b>[stainVariant]</b>): </p> <ol> <li>stained with HE with the standard protocol of the Institute of Pathology, Mannheim (<b>HE</b>)</li> <li>stained too long with HE (<b>longHE</b>)</li> <li>stained too short with HE (<b>shortHE</b>)</li> <li>stained only with Hematoxylin (<b>onlyH</b>)</li> <li>stained only with Eosin (<b>onlyE</b>)</li> <li>stained too long with Hematoxylin (<b>longH</b>)</li> <li>stained too long with Eosin (<b>longE</b>)</li> <li>stained too short with Hematoxylin (<b>shortH</b>)</li> <li>stained too short with Eosin (<b>shortE</b>)</li> </ol> <p> We provided the original whole-slide-images (WSI) in the folder <i>HEV_wsi.zip</i> for each stain-variant. </p> <p> <img src="https://heidata.uni-heidelberg.de/api/access/datafile/4694" alt="wsi_example" width="700"> </p> <p> In addition, for the stain-variants <b>1-5</b> we provide patches (<i>n ~40,000</i> for each set) of size <i>256x256 pixels</i> and split them into 60% train (<i>train_[stainVariant].zip</i>) and 40% test (<i>test_[stainVariant].zip</i>) sets . </p> <p> <img src="https://heidata.uni-heidelberg.de/api/access/datafile/4692" alt="stain_normalization_example" width="600"> </p> <p> Patches from our TumorLymphnode data set for image classification are provided inside <i>tumorLymphnode_patches.zip</i>. It contains <i>~3,600</i> patches of size <i>165x165 pixels</i> for each class normal lymph nodes (<b>normal</b>) and carcinoma infiltration (<b>tumor</b>). </p> <p> The code for our models is available at <a href="http://gitlab.medma.uni-heidelberg.de/digital-pathology/stainTransfer_CycleGAN_pytorch">Gitlab</a>. </p> |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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HEV_stainNormalization_example.png |
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image/png |
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HEV_wsi.zip |
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Notes: |
application/zip |
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HEV_wsi_overview.png |
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image/png |
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test_HE.zip |
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application/zip |
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test_longHE.zip |
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application/zip |
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test_onlyE.zip |
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application/zip |
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test_onlyH.zip |
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application/zip |
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test_shortHE.zip |
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application/zip |
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train_HE.zip |
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application/zip |
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train_longHE.zip |
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application/zip |
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train_onlyE.zip |
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application/zip |
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train_onlyH.zip |
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application/zip |
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train_shortHE.zip |
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application/zip |
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tumorLymphnode_patches.zip |
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Notes: |
application/zip |