Your browser doesn't support javascript.
loading
Stain normalization in digital pathology: Clinical multi-center evaluation of image quality.
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.
Afiliação
  • Michielli N; Biolab, PolitoMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy.
  • Caputo A; Department of Medicine and Surgery, University Hospital of Salerno, Salerno, Italy.
  • Scotto M; Biolab, PolitoMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy.
  • Mogetta A; Biolab, PolitoMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy.
  • Pennisi OAM; Technology Transfer and Industrial Liaison Department, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy.
  • Molinari F; Biolab, PolitoMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy.
  • Balmativola D; Pathology Unit, Humanitas Gradenigo Hospital, Corso Regina Margherita 8, 10153 Turin, Italy.
  • Bosco M; Department of Pathology, Michele and Pietro Ferrero Hospital, 12060 Verduno, Italy.
  • Gambella A; Pathology Unit, Department of Medical Sciences, University of Turin, Via Santena 7, 10126 Turin, Italy.
  • Metovic J; Pathology Unit, Department of Medical Sciences, University of Turin, Via Santena 7, 10126 Turin, Italy.
  • Tota D; Pathology Unit, Department of Medical Sciences, University of Turin, Via Santena 7, 10126 Turin, Italy.
  • Carpenito L; Department of Pathology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
  • Gasparri P; University of Milan, Milan, Italy.
  • Salvi M; UOC di Anatomia Patologica, ASP Catania P.O. "Gravina", Caltagirone, Italy.
J Pathol Inform ; 13: 100145, 2022.
Article em En | MEDLINE | ID: mdl-36268060
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.
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Clinical_trials / Qualitative_research Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Clinical_trials / Qualitative_research Idioma: En Ano de publicação: 2022 Tipo de documento: Article