Deep convolutional gaussian mixture model for stain-color normalization of histopathological images

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Abstract

Automated microscopic analysis of stained histopathological images is degraded by the amount of color and intensity variations in data. This paper presents a novel unsupervised probabilistic approach by integrating a convolutional neural network (CNN) and the Gaussian mixture model (GMM) in a unified framework, which jointly optimizes the modeling and normalizing the color and intensity of hematoxylin- and eosin-stained (H&E) histological images. In contrast to conventional GMM-based methods that are applied only on the color distribution of data for stain color normalization, our proposal learns how to cluster the tissue structures according to their shape and appearance and simultaneously fits a multivariate GMM to the data. This approach is more robust than standard GMM in the presence of strong staining variations because fitting the GMM is conditioned on the appearance of tissue structures in the density channel of an image. Performing a gradient descent optimization in an end-to-end learning, the network learns to maximize the log-likelihood of data given estimated parameters of multivariate Gaussian distributions. Our method does not need ground truth, shape and color assumptions of image contents or manual tuning of parameters and thresholds which makes it applicable to a wide range of histopathological images. Experiments show that our proposed method outperforms the state-of-the-art algorithms in terms of achieving a higher color constancy.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
EditorsGabor Fichtinger, Christos Davatzikos, Carlos Alberola-López, Alejandro F. Frangi, Julia A. Schnabel
Place of PublicationCham
PublisherSpringer
Pages274-282
Number of pages9
ISBN (Electronic)978-3-030-00934-2
ISBN (Print)978-3-030-00933-5
DOIs
Publication statusPublished - 26 Sep 2018
Event21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: 16 Sep 201820 Sep 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11071 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Country/TerritorySpain
CityGranada
Period16/09/1820/09/18

Keywords

  • Computational pathology
  • Convolutional neural network (CNN)
  • Gaussian mixture model (GMM)
  • Stain-color normalization

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