Automatic imaging of Tissue Microarrays using non-linear Microscopy
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- Institutt for fysikk 
St. Olavs hospital has supplied a dataset of 2703 tissue samples from the tumorperiphery from approximately 900 patients organized on tissue microarrays(TMA). In this project we wish to examine all these tissue samples with imageprocessing to determine if second harmonic generation microscope images oftissue can improve classification of cancer type (I, II, III) or in otherwords, cancer aggresiveness. This thesis documents methods which automates themicroscope imaging of TMA and show how images can be correlated to clinicaldata. Datamining methods can then be used on this dataset to look for patternswhich can be used in classifcation. Automated microscope scanning is easy in consept, but the implementationdepends on many aspects of the experimental setup. Some of the aspectsdiscussed in this thesis are: - Develop image analysis algorithms that are robust to experimental variations.- Handle systematic errors like intensity variation and rotation between scanning raster pattern and stage coordinate system.- Automatic stitching of regular spaced images with little signal entropy in seams.- Adjusting z-plane tilt for large area samples with micrometer precision.- Interfacing with commercial Leica software. The focus of this thesis is on TMA and the experimental setup with a LeicaSP8 microscope, but some the aspects listed above are not unique to this contextonly. The conclusions are: - Large area scans should adjust specimen plane to be at even distance to the objective to be time effective and avoid out of focus images.- Using heuristics/constraints improves the reliability to automatic stitching algorithms, failing gracefully on images with little entropy in overlap.- Leica LAS version X 18.104.22.16820 have limited support for automatic microscopy, but it's possible to work around limitations to leverage fully automated TMA-scanning.