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Accurate cell segmentation in microscopic images is a useful tool to analyze individual cell behavior, which helps to diagnose human diseases and development of new treatments. Cell segmentation of individual cells in a microscopic image with many cells in view allows quantification of single cellular features, such as shape or movement patterns, providing rich insight into cellular heterogeneity. Most of the cell segmentation algorithms up till now focus on segmenting cells in the images without classifying the culture of the cell in the images. Discrimination among cell types in microscopic images can lead to a new era of high-throughput cell microscopy. Multiple cell types in co-culture can be easily identified and studying the changes in cell morphology can lead to many applications such as drug treatment. To address this gap, DeepCIS is proposed to detect, segment, and classify the culture of the cells and nucleus in the microscopic images. We have used the EVICAN60 dataset which contains microscopic images from a variety of microscopes having numerous cell cultures, to evaluate the proposed pipeline. To further demonstrate the utility of the DeepCIS, we have designed various experimental settings to uncover its learning potential. We have achieved a mean average precision score of 24.37% for the segmentation task averaged over 30 classes for cell and nucleus.