Improved detection of breast cancer nuclei using modular neural networks

Performance comparison of machine learning methods for prognosis of hormone receptor status in breast cancer tissue samples

We examined the classification and prognostic scoring performances of several computer methods on different feature sets to obtain objective and reproducible analysis of estrogen receptor status in breast cancer tissue samples. Radial basis function network, k-nearest neighborhood search, support vector machines, naive bayes, functional trees, and k-means clustering algorithm were applied to the test datasets. Several features were employed and the classification accuracies of each method for these features were examined. The assessment results of the methods on test images were also experimentally compared with those of two experts. According to the results of our experimental work, a combination of functional trees and the naive bayes classifier gave the best prognostic scores indicating very good kappa agreement values (κ=0.899 and κ=0.949, p<0.001) with the experts. This combination also gave the best dichotomization rate (96.3%) for assessment of estrogen receptor status. Wavelet color features provided better classification accuracy than Laws texture energy and co-occurrence matrix features.

An adaptive resource-allocating network for automated detection, segmentation, and classification of breast cancer nuclei topic area: image processing and recognition

This paper presents a unified image analysis approach for automated detection, segmentation, and classification of breast cancer nuclei using a neural network, which learns to cluster shapes and to classify nuclei. The proposed neural network is incrementally grown by creating a new cluster whenever a previously unseen shape is presented. Each hidden node represents a cluster used as a template to provide faster and more accurate nuclei detection and segmentation. Online learning gives the system improved performance with continued use. The effectiveness of the resulting system is demonstrated on a task of cytological image analysis, with classification of individual nuclei used to diagnose the sample. This demonstrates the potential effectiveness of such a system on diagnostic tasks that require the classification of individual cells.

Colour-texture based image analysis method for assessing the hormone receptors status in breast tissue sections

Hormone receptors have been used in prognosis of breast carcinomas and their positive status is of clinical value in hormonal therapy. Determination of this status is based on the subjective visual inspection of the stained nuclei in the specimens. The aim of this study was the assessment of the estrogen receptor's (ER) positive status of breast carcinomas, by means of colour-texture based image analysis methodology. Twenty two cases of immunohistochemically (IHC) stained breast biopsies were initially assessed by a histopathologist for ER positive status, following a clinical scoring protocol. Custom-designed image analysis software was developed for automatically assessing the ER positive status, employing colour textural features and the k-Nearest Neighbor weighted votes classification algorithm. Computer-based image analysis system resulted in 86.4% overall accuracy and in 0.875 Kendall's coefficient of concordance (p<0.001), ranking correctly 19/22 cases. Colour-texture analysis of IHC stained specimens might have an impact in the quantitative assessment of ER status.

Automated recognition of cell phenotypes in histology images based on membrane- and nuclei-targeting biomarkers

Background

Three-dimensional in vitro culture of cancer cells are used to predict the effects of prospective anti-cancer drugs in vivo. In this study, we present an automated image analysis protocol for detailed morphological protein marker profiling of tumoroid cross section images.

Methods

Histologic cross sections of breast tumoroids developed in co-culture suspensions of breast cancer cell lines, stained for E-cadherin and progesterone receptor, were digitized and pixels in these images were classified into five categories using k-means clustering. Automated segmentation was used to identify image regions composed of cells expressing a given biomarker. Synthesized images were created to check the accuracy of the image processing system.

Results

Accuracy of automated segmentation was over 95% in identifying regions of interest in synthesized images. Image analysis of adjacent histology slides stained, respectively, for Ecad and PR, accurately predicted regions of different cell phenotypes. Image analysis of tumoroid cross sections from different tumoroids obtained under the same co-culture conditions indicated the variation of cellular composition from one tumoroid to another. Variations in the compositions of cross sections obtained from the same tumoroid were established by parallel analysis of Ecad and PR-stained cross section images.

Conclusion

Proposed image analysis methods offer standardized high throughput profiling of molecular anatomy of tumoroids based on both membrane and nuclei markers that is suitable to rapid large scale investigations of anti-cancer compounds for drug development.

Quantitative image analysis in mammary gland biology

In this paper we present a summary of recent quantitative approaches used for the analysis of macro and microscopic images in mammary gland biology. The advantages and disadvantages of whole mount analysis, reconstruction of serial tissue sections and nucleus/cell segmentation of either conventional and confocal images are discussed, as are applications of quantitative image analysis, such as quantification of protein levels or vasculature measurements in normal tissue and cancer. Integration of quantitative imaging into the further study of the mammary gland holds the promise of better understanding its tissue complexity that evolves during development, differentiation and disease.

The classification of oesophageal 24 h pH measurements using a Kohonen self-organizing feature map

Analysis of 24 h oesophageal pH studies can be problematic with many patients asymptomatic during the investigation, despite observations of reflux. The aim of this study was to carry out a cluster analysis of ambulatory pH studies to determine any underlying patterns and classes within the data. The results of 900 24 h pH studies were investigated using the Kohonen self-organizing feature map (SOFM), a neural network that can be used to identify clusters within multidimensional data. The clinical features were presented to the network and the main classes identified. The SOFM-based analysis showed that patients clinically assessed as having symptomatic reflux during the study could be described by four major classifications. The results also showed that the probability of identifying a correlation between symptoms and reflux during an investigation varies from 0.49 to 0.78 for the classes identified. The developed network may be a useful tool in the classification of pH data. The cluster-based technique may offer an alternative to standard statistical techniques for high-dimensional gastrointestinal data and form the basis of an expert system for the automated analysis of pH data.