TVE-F 19028
Examensarbete 15 hp
November 2019
Pixel-Based Algorithms for Data
Analysis in Digital Pathology
Data Analysis of the BOMI2 Redox Dataset,
Teknisk- naturvetenskaplig fakultet UTH-enheten Besöksadress: Ångströmlaboratoriet Lägerhyddsvägen 1 Hus 4, Plan 0 Postadress: Box 536 751 21 Uppsala Telefon: 018 – 471 30 03 Telefax: 018 – 471 30 00 Hemsida: http://www.teknat.uu.se/student
Abstract
Pixel-Based Algorithms for Data Analysis in Digital
Pathology
Michael Wallgren Fjellander
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.
Lp Lr
y1= sqrt(x)1.5
y2
pi
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Image Step I: Compute Knee Points
Step II: Normalise w/ Knee Point
Step III: Compute Rudimentary
Cell Map
Step IV: Compute Weighted Background
Map
