Cell classification in 3D phase-contrast microscopy images via self-organizing maps

Abstract

Cancer cell morphology can be used as an indicator of metastasizing behaviors. To analyze cancer cell morphology, we used 3D phase-contrast microscopy. This is one of the most common imaging modalities for the observation of long-term multi-cellular processes of living cells without phototoxicity and photobleaching, which is common in other fluorescent labeling techniques. However, it also has certain drawbacks at the image level, such as non-uniform illumination and phase-contrast interference rings. Our first step compensates for row-contrast artifacts via single cell detection using intensity-based global segmentation. We extracted cross-sections using principle component analysis; this was due to the interference’s non-symmetric diffusion pattern, which appeared around each individual cell. Then, we analyzed cell morphology by an intensity gradient, considering local peaks as bright ring regions. Finally, we applied a self-organizing map method that has potential viability for cancer cell classification into active and inactive categories.