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Pixel-Based Algorithms for Data Analysis in Digital Pathology: Data Analysis of the BOMI2 Redox Dataset, A Step Away From Cell Segmentation Dependant Methods
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. (Wählby Labs)
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

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.

Place, publisher, year, edition, pages
2019. , p. 49
Series
TVE-F ; 19028
Keywords [en]
Digital Pathology, Image Analysis, Algorithms, Cell Segmentation, Pixel-based Methods, Cancer, Knee Point
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:uu:diva-397504OAI: oai:DiVA.org:uu-397504DiVA, id: diva2:1371877
External cooperation
Rudbecklaboratoriet
Educational program
Master Programme in Engineering Physics
Supervisors
Examiners
Available from: 2019-11-25 Created: 2019-11-21 Last updated: 2019-11-25Bibliographically approved

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