An Improved Cervical Cell Segmentation Method Based on Deep Convolutional Network

The cervical cytology smear test is an effective method for cervical cancer early screening, and segmentation accuracy is essential for computer-aided diagnosis. In this study, an improved cervical nucleus and cytoplasm segmentation method based on a deep convolutional network was proposed. This method consisted of a cellular region proposal and pixel-level segmentation network (CRP-PSN). Data were obtained from the 2014 International Symposium on Biomedical Imaging cervical cell segmentation competition open dataset. In CRP networks, online hard example mining and soft-nonmaximum suppression algorithms were incorporated to solve problems, including background noise, impurities, and other interferences in smear images. For PSN networks, a generative adversarial network-generated adversarial network algorithm and least-squares loss function were used to generate cell region segmentation, thereby improving the cytoplasm segmentation results. Finally, the improved CRP-PSN model was analyzed and compared with other typical cervical cell segmentation methods. The experimental results showed that the proposed model could effectively improve the segmentation accuracy of cytoplasm and nuclei in cervical cytology smear images by 92% and 98.6%, respectively. These findings provided strong support for the application of this method for automated interpretation of cervical cytology smear images and improved diagnostic reliability.