Building robust pathology image analyses with uncertainty quantification.

Humans Image Processing, Computer-Assisted Uncertainty
Microscopy Sensitivity analysis Survival analysis Uncertainty quantification Whole slide image analysis

Journal

Computer methods and programs in biomedicine
ISSN: 1872-7565
Titre abrégé: Comput Methods Programs Biomed
Pays: Ireland
ID NLM: 8506513

Informations de publication

Date de publication:
Sep 2021
Historique:
received: 12 03 2021
accepted: 09 07 2021
pubmed: 2 8 2021
medline: 17 8 2021
entrez: 1 8 2021
Statut: ppublish

Résumé

Computerized pathology image analysis is an important tool in research and clinical settings, which enables quantitative tissue characterization and can assist a pathologist's evaluation. The aim of our study is to systematically quantify and minimize uncertainty in output of computer based pathology image analysis. Uncertainty quantification (UQ) and sensitivity analysis (SA) methods, such as Variance-Based Decomposition (VBD) and Morris One-At-a-Time (MOAT), are employed to track and quantify uncertainty in a real-world application with large Whole Slide Imaging datasets - 943 Breast Invasive Carcinoma (BRCA) and 381 Lung Squamous Cell Carcinoma (LUSC) patients. Because these studies are compute intensive, high-performance computing systems and efficient UQ/SA methods were combined to provide efficient execution. UQ/SA has been able to highlight parameters of the application that impact the results, as well as nuclear features that carry most of the uncertainty. Using this information, we built a method for selecting stable features that minimize application output uncertainty. The results show that input parameter variations significantly impact all stages (segmentation, feature computation, and survival analysis) of the use case application. We then identified and classified features according to their robustness to parameter variation, and using the proposed features selection strategy, for instance, patient grouping stability in survival analysis has been improved from in 17% and 34% for BRCA and LUSC, respectively. This strategy created more robust analyses, demonstrating that SA and UQ are important methods that may increase confidence digital pathology.

Sections du résumé

BACKGROUND AND OBJECTIVE OBJECTIVE
Computerized pathology image analysis is an important tool in research and clinical settings, which enables quantitative tissue characterization and can assist a pathologist's evaluation. The aim of our study is to systematically quantify and minimize uncertainty in output of computer based pathology image analysis.
METHODS METHODS
Uncertainty quantification (UQ) and sensitivity analysis (SA) methods, such as Variance-Based Decomposition (VBD) and Morris One-At-a-Time (MOAT), are employed to track and quantify uncertainty in a real-world application with large Whole Slide Imaging datasets - 943 Breast Invasive Carcinoma (BRCA) and 381 Lung Squamous Cell Carcinoma (LUSC) patients. Because these studies are compute intensive, high-performance computing systems and efficient UQ/SA methods were combined to provide efficient execution. UQ/SA has been able to highlight parameters of the application that impact the results, as well as nuclear features that carry most of the uncertainty. Using this information, we built a method for selecting stable features that minimize application output uncertainty.
RESULTS RESULTS
The results show that input parameter variations significantly impact all stages (segmentation, feature computation, and survival analysis) of the use case application. We then identified and classified features according to their robustness to parameter variation, and using the proposed features selection strategy, for instance, patient grouping stability in survival analysis has been improved from in 17% and 34% for BRCA and LUSC, respectively.
CONCLUSIONS CONCLUSIONS
This strategy created more robust analyses, demonstrating that SA and UQ are important methods that may increase confidence digital pathology.

Identifiants

DOI: 10.1016/j.cmpb.2021.106291 PMID: 34333205
pubmed: 34333205
pii: S0169-2607(21)00365-5
doi: 10.1016/j.cmpb.2021.106291
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

106291

Informations de copyright

Copyright © 2021 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest Authors declare that they have no conflict of interest.

Auteurs

Jeremias Gomes (J)

Department of Computer Science, University of Brasília, Brasília, Brazil.

Jun Kong (J)

Biomedical Informatics Department, Emory University, Atlanta, USA; Department of Biomedical Engineering, Emory-Georgia Institute of Technology, Atlanta, USA; Department of Mathematics and Statistics, Georgia State University, Atlanta, USA.

Tahsin Kurc (T)

Biomedical Informatics Department, Stony Brook University, Stony Brook, USA; Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, USA.

Alba C M A Melo (ACMA)

Department of Computer Science, University of Brasília, Brasília, Brazil.

Renato Ferreira (R)

Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.

Joel H Saltz (JH)

Biomedical Informatics Department, Stony Brook University, Stony Brook, USA.

George Teodoro (G)

Department of Computer Science, University of Brasília, Brasília, Brazil; Biomedical Informatics Department, Stony Brook University, Stony Brook, USA; Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil. Electronic address: teodoro@dcc.ufmg.br.

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Classifications MeSH

questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Building robust pathology image analyses with...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Traitement d'image par ordinateur › Building robust pathology image analyses with...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Statistiques comme sujet › Probabilité › Incertitude › Building robust pathology image analyses with...