Abstract
Separating cells from the background in microscopy images is the critical step in image processing pipeline for the study of single cell life cycle. Live cell imaging experiments involve thousands of cells and images taken for a few days, which results in huge data generation. Automatic analysis of such images is essential rather than performing analysis manually. The challenges involved are non-uniform illumination of the image, different types of cell lines to be studied, large curation time required and analysis of large data to name a few. In this work we present a image processing pipeline using a convolutional neural network (CNN) model followed by thresholding and morphological operations for segmenting the NIH 3T3 cells in microscopic images. The segmentation results are evaluated by comparing them with the ground truth images. The proposed methodology gave a Dice index of 0.93 on a stack of 238 phase contrast images. Further, we show that CNN based approach performs superior to conventional image processing segmentation methods on phase contrast images of NIH 3T3 cells.
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Kakumani, A.K., Padma Sree, L. (2020). A Deep Learning Approach for Segmenting Time-Lapse Phase Contrast Images of NIH 3T3 Fibroblast Cells. In: Smys, S., Iliyasu, A.M., Bestak, R., Shi, F. (eds) New Trends in Computational Vision and Bio-inspired Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-41862-5_86
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DOI: https://doi.org/10.1007/978-3-030-41862-5_86
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