One way to solve under-determined image separation is to use statistical information about the type of data to be decomposed. In this dissertation, we propose a two stage method for cervical cell separation. In the first stage, we propose a CNN-based multi-layer random walker image segmentation method. The results of image segmentation at the first stage are then used as the side information for the cervical cell separation in the second stage. Convolutional neural networks (CNNs) are recently used in computer vision applications such as image segmentation. One of the biggest advantages of CNNs is that they extract important features automatically from the data. However, CNNs usually use a high number of data for training but it is not always possible to find enough training data for some applications. Also, CNNs are good at generalizing the training, but not for finding the accurate edges of cervical cells at the current implementations for cervical cell segmentation. One solution to improve CNN segmentation results is to use a post-processing method as edge refinement. Random walker image segmentation method is a graph-based image segmentation method that is good at region growing; so, it can be used as the refinement step. However, random walker is sensitive to initial setup, so if the seeds are not extracted correctly, the final segmentation results will be poor. In this dissertation we use a CNN-based random walker image segmentation approach for cervical cell segmentation. Different from other methods that use CNN binary segmentation results for fine tuning, CNN probabilistic map is utilized to guide random walker image segmentation method at the refinement step. In the second stage, we design a new CNN, and we use the input image along with the segmentation masks from our first stage for cervical cell separation. To the best of our knowledge, there is no work done for overlapping cervical cell separation. We apply our two-stage cervical cell separation algorithm on synthetic cervical cytology images which can open an opportunity to early cervical cancer detection. Results show segmentation accuracy of greater than 99:5% and separation SSIM score of greater than 0:93.