Self-supervised semantic segmentation of retinal pigment epithelium cells in flatmount fluorescent microscopy images.

ABSTRACT:

Motivation

Morphological analyses with flatmount fluorescent images are essential to retinal pigment epithelial (RPE) aging studies and thus require accurate RPE cell segmentation. Although rapid technology advances in deep learning semantic segmentation have achieved great success in many biomedical research, the performance of these supervised learning methods for RPE cell segmentation is still limited by inadequate training data with high-quality annotations.

Results

To address this problem, we develop a Self-Supervised Semantic Segmentation (S4) method that utilizes a self-supervised learning strategy to train a semantic segmentation network with an encoder-decoder architecture. We employ a reconstruction and a pairwise representation loss to make the encoder extract str

SUBMITTER: Yu H 

PROVIDER: S-EPMC10139776 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature