Morphological Estimation of Cellularity on Neo-Adjuvant Treated Breast Cancer Histological Images

Abstract

This paper describes a methodology that extracts key morphological features from histological breast cancer images in order to automatically assess Tumour Cellularity (TC) in Neo-Adjuvant treatment (NAT) patients. The response to NAT gives information on therapy efficacy and it is measured by the residual cancer burden index, which is composed of two metrics: TC and the assessment of lymph nodes. The data consist of whole slide images (WSIs) of breast tissue stained with Hematoxylin and Eosin (H&E) released in the 2019 SPIE Breast Challenge. The methodology proposed is based on traditional computer vision methods (K-means, watershed segmentation, Otsu’s binarisation, and morphological operations), implementing colour separation, segmentation, and feature extraction. Correlation between morphological features and the residual TC after a NAT treatment was examined. Linear regression and statistical methods were used and twenty-two key morphological parameters from the nuclei, epithelial region, and the full image were extracted. Subsequently, an automated TC assessment that was based on Machine Learning (ML) algorithms was implemented and trained with only selected key parameters. The methodology was validated with the score assigned by two pathologists through the intra-class correlation coefficient (ICC). The selection of key morphological parameters improved the results reported over other ML methodologies and it was very close to deep learning methodologies. These results are encouraging, as a traditionally-trained ML algorithm can be useful when limited training data are available preventing the use of deep learning approaches.

Efficient Approach to Color Image Segmentation Based on Multilevel Thresholding Using EMO Algorithm by Considering Spatial Contextual Information

“…(iii) Mean square error (MSE), a lower MSE value illustrates better segmentation; it computes the average of the square of the error. (iv) Structural similarity (SSIM), this parameter gives the level of similarity between the segmented and input image under test; a greater value of SSIM [39] indicates a better segmentation effect; it is in the range from −1 to +1. (v) Feature similarity (FSIM), this is similar to SSIM, which indicates degradation of image quality; it ranges [−1, 1]; a high value of FSIM means better segmentation of the color image.…”

Modelling the Tumour Microenvironment, but What Exactly Do We Mean by “Model”?

“…What is modelled is not the cells or the cancer itself, but rather derived features, like the shape of a cell or a vessel [72], the movement of cells or fluorescent intensity. There does not need to exist an underlying mathematical abstraction of cancer or a biological process in these methodologies, but the information extracted relates to conditions of the cancer, like the cellularity [166].…”