The authors have proposed a cell image analysis system that offers the mechanisms of cell segmentation, tracking, and fluorescence analysis, where the segmentation step is the crucial one that dominates the overall performance. This paper proposes a CNN-based segmentation technique for cell images to improve the segmentation accuracy in the analysis system. In the segmentation step, the division timing of cells is important together with a geometrical accuracy of segmented cells. The proposed technique makes use of two sets of fluorescent (FL) cell images as well as common bright-field (BF) images to boost the accuracy of the division timing. These image sets are fed into an extended version of a multi-input U-Net to generate accurate cell markers utilized as seeds in the watershed postprocessing. The experimental results demonstrate that the proposed technique gives a satisfactory accuracy in terms both of the geometrical shape and division timing in the cell segmentation task.
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