Image Analysis of Circulating Tumour Cell Clusters from Imaging Flow Cytometry Data

Abstract: Circulating tumour cells (CTCs) are cancer cells that have entered the circulation of the body breaking free from their primary tumour and that can act as progenitors of metastasis. At the time of writing, a study on a novel method to detect and count CTCs using imaging flow cytometry (IFC) is being conducted at Lund University. In the study, a problem was found where CTCs clustered with normal white blood cells (WBCs) were not detected as CTC candidates. These CTCs were not detected because the analysis software treated clusters the same as single cells. The rarity of CTCs in blood means it is important to detect every single one in a sample. This thesis aimed to develop an algorithm that could detect CTC-WBC clusters in IFC data of prostate cancer patient samples. An algorithm that could automate the detection of CTC candidates would simplify the present process which suffer from excessive manual assessment. The main problem to be solved was to segment the different cells in the clusters from each other in the images. An algorithm to detect CTC-WBC clusters in IFC data was proposed and was initially tested on three patient datasets. The algorithm showed stable segmentation results. The problem of segmenting cells was solved by using an Otsu threshold and watershed approach on images of cells stained with the nucleic fluorescent marker DAPI. The segmented regions could then be used to examine the fluorescent intensity of other stains within the regions. The initial results of CTC detection were promising. The number of candidates to manually assess to find CTC-WBC clusters was greatly reduced and is now at a manageable level. At the time of writing this, the program is deployed and ready for use in the continuation of the study.