دورية أكاديمية
Cross-platform dataset of multiplex fluorescent cellular object image annotations
| العنوان: | Cross-platform dataset of multiplex fluorescent cellular object image annotations |
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| المؤلفون: | Aleynick, Nathaniel, Li, Yanyun, Xie, Yubin, Zhang, Mianlei, Posner, Andrew, Roshal, Lev, Pe’er, Dana, Vanguri, Rami S., Hollmann, Travis J. |
| المساهمون: | U.S. Department of Health & Human Services | NIH | National Cancer Institute, Parker Institute for Cancer Immunotherapy |
| المصدر: | Scientific Data ; volume 10, issue 1 ; ISSN 2052-4463 |
| بيانات النشر: | Springer Science and Business Media LLC |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | Library and Information Sciences, Statistics, Probability and Uncertainty, Computer Science Applications, Education, Information Systems, Statistics and Probability |
| الوصف: | Defining cellular and subcellular structures in images, referred to as cell segmentation, is an outstanding obstacle to scalable single-cell analysis of multiplex imaging data. While advances in machine learning-based segmentation have led to potentially robust solutions, such algorithms typically rely on large amounts of example annotations, known as training data. Datasets consisting of annotations which are thoroughly assessed for quality are rarely released to the public. As a result, there is a lack of widely available, annotated data suitable for benchmarking and algorithm development. To address this unmet need, we release 105,774 primarily oncological cellular annotations concentrating on tumor and immune cells using over 40 antibody markers spanning three fluorescent imaging platforms, over a dozen tissue types and across various cellular morphologies. We use readily available annotation techniques to provide a modifiable community data set with the goal of advancing cellular segmentation for the greater imaging community. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| DOI: | 10.1038/s41597-023-02108-z |
| الإتاحة: | https://doi.org/10.1038/s41597-023-02108-zTest https://www.nature.com/articles/s41597-023-02108-z.pdfTest https://www.nature.com/articles/s41597-023-02108-zTest |
| حقوق: | https://creativecommons.org/licenses/by/4.0Test ; https://creativecommons.org/licenses/by/4.0Test |
| رقم الانضمام: | edsbas.89517C42 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1038/s41597-023-02108-z |
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