A Coarsetofine Cascading Model for Cataract Nuclear Segmentation in Slitlamp Photographs A nuclear cataract is an agerelated chronic and priority ophthalmic disease in which a clouding of the lens in the human eye affects vision. Automatic segmentation of nuclear region based on slitlamp photographs is a basic step for computeraided diagnosis such as nuclear cataract grading. However, slitlamp photographs collected from a clinic scenario often have complex background containing the eyelids, sclera and cornea with spectral highlights. The existing efforts using traditional image processing that have unsatisfactory results, and the deep learning method using standard Faster RCNN tends to obtain a bigger nuclear contour. In this paper, we propose a coarsetofine deep learning solution to localize nuclear regions by cascading the Faster RCNN in a twostage framework. First, a nuclear ROI (region of interest) predictor is pretrained to localize a rough position and remove complex backgrounds. Then, a fine nuclear locator is applied to predict a more compact nuclear bounding box. Finally, an ellipselike nuclear contour is fitted based on its bounding box. Evaluated on a clinical dataset of 884 slitlamp photographs, the proposed method outperforms the stateoftheart, improving the overlapping rate (IoU) by 0.33% from 67.98% to 68.31%, and increasing the success rate by 2.55% from 85.71% to 88.26%.

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.