Michielli Nicola, Caputo Alessandro, Scotto Manuela, Mogetta Alessandro, Pennisi Orazio Antonino Maria, Molinari Filippo, Balmativola Davide, Bosco Martino, Gambella Alessandro, Metovic Jasna, Tota Daniele, Carpenito Laura, Gasparri Paolo, Salvi Massimo
Biolab, PolitoMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy.
Department of Medicine and Surgery, University Hospital of Salerno, Salerno, Italy.
J Pathol Inform. 2022 Sep 24;13:100145. doi: 10.1016/j.jpi.2022.100145. eCollection 2022.
In digital pathology, the final appearance of digitized images is affected by several factors, resulting in stain color and intensity variation. Stain normalization is an innovative solution to overcome stain variability. However, the validation of color normalization tools has been assessed only from a quantitative perspective, through the computation of similarity metrics between the original and normalized images. To the best of our knowledge, no works investigate the impact of normalization on the pathologist's evaluation. The objective of this paper is to propose a multi-tissue (i.e., breast, colon, liver, lung, and prostate) and multi-center qualitative analysis of a stain normalization tool with the involvement of pathologists with different years of experience. Two qualitative studies were carried out for this purpose: (i) a first study focused on the analysis of the perceived image quality and absence of significant image artifacts after the normalization process; (ii) a second study focused on the clinical score of the normalized image with respect to the original one. The results of the first study prove the high quality of the normalized image with a low impact artifact generation, while the second study demonstrates the superiority of the normalized image with respect to the original one in clinical practice. The normalization process can help both to reduce variability due to tissue staining procedures and facilitate the pathologist in the histological examination. The experimental results obtained in this work are encouraging and can justify the use of a stain normalization tool in clinical routine.
在数字病理学中，数字化图像的最终外观受多种因素影响，导致染色颜色和强度变化。染色归一化是克服染色变异性的一种创新解决方案。然而，颜色归一化工具的验证仅从定量角度进行了评估，即通过计算原始图像和归一化图像之间的相似性指标。据我们所知，尚无研究探讨归一化对病理学家评估的影响。本文的目的是在不同经验年限的病理学家参与下，对一种染色归一化工具进行多组织（即乳腺、结肠、肝脏、肺和前列腺）和多中心的定性分析。为此进行了两项定性研究：（i）第一项研究侧重于分析归一化过程后感知到的图像质量以及是否存在明显的图像伪影；（ii）第二项研究侧重于归一化图像相对于原始图像的临床评分。第一项研究的结果证明了归一化图像的高质量以及伪影产生的低影响，而第二项研究表明在临床实践中归一化图像相对于原始图像具有优越性。归一化过程既有助于减少因组织染色程序导致的变异性，又便于病理学家进行组织学检查。这项工作中获得的实验结果令人鼓舞，可为在临床常规中使用染色归一化工具提供依据。
