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MRF‐ANN: a machine learning approach for automated ER scoring of breast cancer immunohistochemical images
Author(s) -
MUNGLE T.,
TEWARY S.,
DAS D.K.,
ARUN I.,
BASAK B.,
AGARWAL S.,
AHMED R.,
CHATTERJEE S.,
CHAKRABORTY C.
Publication year - 2017
Publication title -
journal of microscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.569
H-Index - 111
eISSN - 1365-2818
pISSN - 0022-2720
DOI - 10.1111/jmi.12552
Subject(s) - artificial intelligence , computer science , estrogen receptor , intraclass correlation , segmentation , oestrogen receptor , breast cancer , machine learning , artificial neural network , progesterone receptor , correlation , pattern recognition (psychology) , pathology , medicine , cancer , mathematics , statistics , geometry , psychometrics
Summary Molecular pathology, especially immunohistochemistry, plays an important role in evaluating hormone receptor status along with diagnosis of breast cancer. Time‐consumption and inter‐/intraobserver variability are major hindrances for evaluating the receptor score. In view of this, the paper proposes an automated Allred Scoring methodology for estrogen receptor (ER). White balancing is used to normalize the colour image taking into consideration colour variation during staining in different labs. Markov random field model with expectation‐maximization optimization is employed to segment the ER cells. The proposed segmentation methodology is found to have F‐measure 0.95. Artificial neural network is subsequently used to obtain intensity‐based score for ER cells, from pixel colour intensity features. Simultaneously, proportion score – percentage of ER positive cells is computed via cell counting. The final ER score is computed by adding intensity and proportion scores – a standard Allred scoring system followed by pathologists. The classification accuracy for classification of cells by classifier in terms of F‐measure is 0.9626. The problem of subjective interobserver ability is addressed by quantifying ER score from two expert pathologist and proposed methodology. The intraclass correlation achieved is greater than 0.90. The study has potential advantage of assisting pathologist in decision making over manual procedure and could evolve as a part of automated decision support system with other receptor scoring/analysis procedure.