Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.
Diagn Pathol. 2012 Mar 21;7:29. doi: 10.1186/1746-1596-7-29.
Digital whole-slide scanning of tissue specimens produces large images demanding increasing storing capacity. To reduce the need of extensive data storage systems image files can be compressed and scaled down. The aim of this article is to study the effect of different levels of image compression and scaling on automated image analysis of immunohistochemical (IHC) stainings and automated tumor segmentation.
Two tissue microarray (TMA) slides containing 800 samples of breast cancer tissue immunostained against Ki-67 protein and two TMA slides containing 144 samples of colorectal cancer immunostained against EGFR were digitized with a whole-slide scanner. The TMA images were JPEG2000 wavelet compressed with four compression ratios: lossless, and 1:12, 1:25 and 1:50 lossy compression. Each of the compressed breast cancer images was furthermore scaled down either to 1:1, 1:2, 1:4, 1:8, 1:16, 1:32, 1:64 or 1:128. Breast cancer images were analyzed using an algorithm that quantitates the extent of staining in Ki-67 immunostained images, and EGFR immunostained colorectal cancer images were analyzed with an automated tumor segmentation algorithm. The automated tools were validated by comparing the results from losslessly compressed and non-scaled images with results from conventional visual assessments. Percentage agreement and kappa statistics were calculated between results from compressed and scaled images and results from lossless and non-scaled images.
Both of the studied image analysis methods showed good agreement between visual and automated results. In the automated IHC quantification, an agreement of over 98% and a kappa value of over 0.96 was observed between losslessly compressed and non-scaled images and combined compression ratios up to 1:50 and scaling down to 1:8. In automated tumor segmentation, an agreement of over 97% and a kappa value of over 0.93 was observed between losslessly compressed images and compression ratios up to 1:25.
The results of this study suggest that images stored for assessment of the extent of immunohistochemical staining can be compressed and scaled significantly, and images of tumors to be segmented can be compressed without compromising computer-assisted analysis results using studied methods.
The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/2442925476534995.
组织标本的数字全玻片扫描会产生大量图像，这需要不断增加存储容量。为了减少对大型数据存储系统的需求，可以对图像文件进行压缩和缩小。本文的目的是研究不同程度的图像压缩和缩放对免疫组织化学（IHC）染色的自动图像分析和自动肿瘤分割的影响。
使用全玻片扫描仪对 2 张包含 800 例乳腺癌组织 Ki-67 蛋白免疫染色的组织微阵列（TMA）载玻片和 2 张包含 144 例结直肠癌 EGFR 免疫染色的 TMA 载玻片进行数字化。TMA 图像使用 JPEG2000 小波进行压缩，压缩比为无损和 1:12、1:25 和 1:50 有损压缩。对每个压缩的乳腺癌图像进行进一步的缩放，比例为 1:1、1:2、1:4、1:8、1:16、1:32、1:64 或 1:128。使用一种算法对 Ki-67 免疫染色图像中的染色程度进行分析，该算法对 EGFR 免疫染色的结直肠癌图像进行分析。通过将无损压缩和非缩放图像的结果与传统视觉评估的结果进行比较，对自动工具进行了验证。在压缩和缩放图像的结果与无损和非缩放图像的结果之间计算了百分比一致性和kappa 统计数据。
两种研究的图像分析方法在视觉和自动结果之间都显示出很好的一致性。在自动 IHC 定量中，在无损压缩和非缩放图像以及组合压缩比高达 1:50 和缩小至 1:8 之间观察到超过 98%的一致性和超过 0.96 的kappa 值。在自动肿瘤分割中，在无损压缩图像和高达 1:25 的压缩比之间观察到超过 97%的一致性和超过 0.93 的 kappa 值。
本研究结果表明，用于评估免疫组织化学染色程度的图像可以显著压缩和缩放，并且可以在不影响使用研究方法的计算机辅助分析结果的情况下对要分割的肿瘤图像进行压缩。
本文的虚拟幻灯片可在此处找到：http://www.diagnosticpathology.diagnomx.eu/vs/2442925476534995。
