# Residual Pyramid FCN for Robust Follicle Segmentation

arXiv: https://arxiv.org/abs/1901.03760 · HTML: https://mlsanity.com/paper/1901.03760/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2019.

## Built on by (most cited first, 1 of 1)

- [A Comparative Study on 1.5T-3T MRI Conversion through Deep Neural Network Models](https://mlsanity.com/paper/2210.06362/) (2022)

## Abstract (arXiv)

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.