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.

Stain normalization in digital pathology: Clinical multi-center evaluation of image quality / Michielli, N.; Caputo, A.; Scotto, M.; Mogetta, A.; Pennisi, O. A. M.; Molinari, F.; Balmativola, D.; Bosco, M.; Gambella, A.; Metovic, J.; Tota, D.; Carpenito, L.; Gasparri, P.; Salvi, M.. - In: JOURNAL OF PATHOLOGY INFORMATICS. - ISSN 2229-5089. - 13:(2022), p. 100145. [10.1016/j.jpi.2022.100145]

Stain normalization in digital pathology: Clinical multi-center evaluation of image quality

Caputo A.;
2022

Abstract

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.
2022
Stain normalization in digital pathology: Clinical multi-center evaluation of image quality / Michielli, N.; Caputo, A.; Scotto, M.; Mogetta, A.; Pennisi, O. A. M.; Molinari, F.; Balmativola, D.; Bosco, M.; Gambella, A.; Metovic, J.; Tota, D.; Carpenito, L.; Gasparri, P.; Salvi, M.. - In: JOURNAL OF PATHOLOGY INFORMATICS. - ISSN 2229-5089. - 13:(2022), p. 100145. [10.1016/j.jpi.2022.100145]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/918214
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