CellSegNet : an adaptive multi-resolution hybrid network for cell segmentation

Image segmentation is one primary area in which deep learning has made a major contribution to medical image analysis. The automatic and precise segmentation of cells in cytopathology, or cytology for short, can significantly reduce the diagnostic work from pathologists. The biomedical image segmentation task routinely employs an encoder-decoder structure, e.g. U-Net, in which the receptive field is often fixed. However, to achieve a better morphological segmentation performance, we empirically found receptive field should be correlated with cell size by differential structures. In this paper, we proposed a novel deep-learning based cytology image segmentation model, namely CellSegNet. This model can dynamically catalog cells by their size, and subsequently fit to their corresponding light-weight structures, characterized with weighted multiple receptive fields to better retrieve feature extraction. The proposed model can outperform other state-of-art biomedical image segmentation networks with observable improvements. Moreover, the high interpretability of the proposed model can be flexibly extended to other cytology datasets. The source code in the experiments and part of our collection of cervical images are publicly available at https://github.com/SJTU-AI-GPU/CellSegNet.