Pixel-Based Algorithms for DataAnalysis in Digital Pathology : Data Analysis of the BOMI2 Redox Dataset,A Step Away From Cell Segmentation Dependant Methods

Abstract: In this project report a novel pixel-based approach to digital pathology is proposed. The algorithm directly decides the class of single pixels in an image without needing the larger context of neighbouring pixels. This allows researchers to circumvent complications that might arise from using classical cell segmentation methods based around counting cells - which then relies on the cell segmentation being close to perfect. Such issues are avoided by pixel-based approaches by instead directly measuring total area. The algorithm is tested on the BOMI2 Redox dataset consisting of 79 samples of multi-spectral images from lung cancer patients. The results of the algorithm are compared against ground truth data in the form of RNA sequencing data from the same patient cores as the images are taken. The algorithm achieves Spearman correlations in the range of R = [0.4,0.6], thereby serving as an initial testament to the validity of pixel-based methods. Furthermore an automatic method for deciding biomarker threshold values is proposed, based around finding the knee point of the biomarker histogram. The threshold values found by the algorithm on the BOMI2 Redox data set are reasonable. The method opens up for a standardised way of deciding thresholds in digital pathology, allowing easier comparison between research results from different researchers.