Deep Learning Methods for Image Decomposition of Cervical Cells

Abstract

One way to solve under-determined image decomposition is to use statistical information about the type of data to be decomposed. This information can be obtained by a deep learning where convolutional neural networks (CNN) are a subset recently used widely in image processing. In this paper, we have designed a two-stage CNN that takes cytology images of overlapped cervical cells and attempts to separate the cell images. In the first stage, we designed a CNN to segment overlapping cells. In the second stage, we designed a CNN that uses this segmentation and the original image to separate the regions. We implemented a CNN similar to U-Net for image segmentation and implemented a new network for the image separation. To train and test the proposed networks, we simulated 50000 cervical cell cytology images by overlaying individual images of real cervical cells using the Beer-Lambert law. Of these 50000 images, we used 49000 images for training and evaluated the method with 1000 test images. Results on these synthetic images give more than 97% segmentation accuracy and gives decomposition SSIM scores of more than 0.99 and PSNR score of more than 30 dB. Despite these positive results, the permutation problem that commonly effects signal separation occasionally occurred resulting in some cell structure mis-separation (for example, one cell given two nucleoli and the other given none). In addition, when the segmentation was poor from the first stage, the resulting separation was poor.