Novel Image Analysis Methods for Quantification of DNA Microballs from Fluorescence Microscopy

Sammanfattning

Gene editing techniques have been emerging rapidly through the years, and with this trend comes the great responsibility of making sure the edits are correct. One way to safeguard against mistakes in the edits is to measure gene editing efficiency. Countagen’s GeneAbacus does just that, it calculates the gene editing efficiency of CRISPR edits. A key aspect of the GeneAbacus workflow involves quantifying DNA microballs captured in fluorescence microscopy images. This thesis delves into novel image analysis pipelines aimed at optimizing this task. Six image processing techniques (Maximum Intensity Projection (MIP), white top hat transform, Contrast Limited Adaptive Histogram Equalisation (CLAHE), edge enhancement filter, Gaussian Blur, and unsharp masking) along with two object segmentation models (Segment Anything (SAM) and SAM for Microscopy (MicroSAM)) were implemented. They underwent evaluation in two stages: first, through an ablation study of the preprocessing techniques, and then by computing R2 values and log-log plot slopes on different datasets. The evaluation resulted in the selection of MicroSAM with white top hat transform, Gaussian blur and unsharp masking, yielding an average slope value of 0.698 and an average R2 value of 0.8724.