Prior Guided Segmentation and Nuclei Feature Based Abnormality Detection in Cervical Cells Computerassisted techniques for cytological analysis and abnormality detection, can help to early diagnose anomalies in cervical smear images. Cell nuclei carry substantial evidence of precancerous changes, thus morphological properties of nuclei are important for accurate diagnostic decision. A novel nucleus featurebased cervical cell classification framework is proposed in this study. Prior guided segmentation algorithms are employed to accurately detect and segment nucleus. Fuzzy entropy based feature selection technique is used to select most discriminatory features, extracted from segmented nucleus. Five classifiers: knearest neighbor (KNN), linear discriminant analysis (LDA), Ensemble, and support vector machine with linear kernel (SVMlinear) and radial basis function kernel (SVMRBF), are used to detect abnormality in cervical cells. The proposed framework is evaluated using Herlev dataset of 917 cervical cell images and compared with stateoftheart methods. Results indicate that the proposed framework matches the performance of recent techniques, while segmenting nucleus and classifying Pap smear images using only 10 nucleus features. Therefore, the proposed abnormality detection framework can assist cytologists in computerized cervical cell analysis, and help with early discovery of any anomaly that may lead to cervical cancer.

PGUnet: Progressive Growing of Unet for Automated Cervical Nuclei Segmentation. Automated cervical nucleus segmentation based on deep learning can effectively improve the quantitative analysis of cervical cancer. However, accurate nuclei segmentation is still challenging. The classic Unet has not achieved satisfactory results on this task, because it mixes the information of different scales that affect each other, which limits the segmentation accuracy of the model. To solve this problem, we propose a progressive growing Unet (PGUnet) model, which uses two paradigms to extract image features at different scales in a more independent way. First, we add residual modules between different scales of Unet, which enforces the model to learn the approximate shape of the annotation in the coarser scale, and to learn the residual between the annotation and the approximate shape in the finer scale. Second, we start to train the model with the coarsest part and then progressively add finer part to the training until the full model is included. When we train a finer part, we will reduce the learning rate of the previous coarser part, which further ensures that the model independently extracts information from different scales. We conduct several comparative experiments on the Herlev dataset. The experimental results show that the PGUnet has superior accuracy than the previous stateoftheart methods on cervical nuclei segmentation.