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.
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