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.
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Kang, MS., Kim, HR., Kim, MH. (2014). Cell Classification in 3D Phase-Contrast Microscopy Images via Self-Organizing Maps. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_63
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DOI: https://doi.org/10.1007/978-3-319-14364-4_63
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14363-7
Online ISBN: 978-3-319-14364-4
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