  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 U-net 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 U-net
(PGU-net+) 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 U-net, 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 PGU-net+ has
superior accuracy than the previous state-of-the-art methods on cervical nuclei
segmentation.
