In this paper, we propose a pyramid network structure to improve the FCN-based segmentation solutions and apply it to label thyroid follicles in histology images. Our design is based on the notion that a hierarchical updating scheme, if properly implemented, can help FCNs capture the major objects, as well as structure details in an image. To this end, we devise a residual module to be mounted on consecutive network layers, through which pixel labels would be propagated from the coarsest layer towards the finest layer in a bottom-up fashion. We add five residual units along the decoding path of a modified U-Net to make our segmentation network, Res-Seg-Net. Experiments demonstrate that the multi-resolution set-up in our model is effective in producing segmentations with improved accuracy and robustness.
@article{arxiv.1901.03760,
title = {Residual Pyramid FCN for Robust Follicle Segmentation},
author = {Zhewei Wang and Weizhen Cai and Charles D. Smith and Noriko Kantake and Thomas J. Rosol and Jundong Liu},
journal= {arXiv preprint arXiv:1901.03760},
year = {2019}
}