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HistoBreast,一组 Haematoxylin 和 Eosin 染色的乳腺组织明场显微镜图像。

HISTOBREAST, a collection of brightfield microscopy images of Haematoxylin and Eosin stained breast tissue.

机构信息

Center for Microscopy-Microanalysis and Information Processing, Politehnica University of Bucharest, Bucharest, Romania.

School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.

出版信息

Sci Data. 2020 Jun 5;7(1):169. doi: 10.1038/s41597-020-0500-0.

Abstract

Modern histopathology workflows rely on the digitization of histology slides. The quality of the resulting digital representations, in the form of histology slide image mosaics, depends on various specific acquisition conditions and on the image processing steps that underlie the generation of the final mosaic, e.g. registration and blending of the contained image tiles. We introduce HISTOBREAST, an extensive collection of brightfield microscopy images that we collected in a principled manner under different acquisition conditions on Haematoxylin - Eosin (H&E) stained breast tissue. HISTOBREAST is comprised of neighbour image tiles and ensemble of mosaics composed from different combinations of the available image tiles, exhibiting progressively degraded quality levels. HISTOBREAST can be used to benchmark image processing and computer vision techniques with respect to their robustness to image modifications specific to brightfield microscopy of H&E stained tissues. Furthermore, HISTOBREAST can serve in the development of new image processing methods, with the purpose of ensuring robustness to typical image artefacts that raise interpretation problems for expert histopathologists and affect the results of computerized image analysis.

摘要

现代组织病理学工作流程依赖于组织学幻灯片的数字化。由此产生的数字表示形式(即组织学幻灯片图像镶嵌图)的质量取决于各种特定的采集条件,以及生成最终镶嵌图所基于的图像处理步骤,例如包含的图像瓦片的配准和混合。我们引入了 HISTOBREAST,这是一个广泛的明场显微镜图像集合,我们在苏木精-伊红(H&E)染色的乳腺组织上以不同的采集条件有原则地收集。HISTOBREAST 由相邻的图像瓦片和由可用图像瓦片的不同组合组成的镶嵌图组成,具有逐渐降低的质量水平。HISTOBREAST 可用于针对特定于 H&E 染色组织的明场显微镜的图像修改的稳健性,对图像处理和计算机视觉技术进行基准测试。此外,HISTOBREAST 可用于开发新的图像处理方法,目的是确保对引起专家组织病理学家解释问题并影响计算机化图像分析结果的典型图像伪影具有稳健性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/799e/7275059/e0d229676150/41597_2020_500_Fig1_HTML.jpg

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