Self2Seg: Single-Image Self-Supervised Joint Segmentation and Denoising

TL;DR

Self2Seg introduces a self-supervised approach that jointly segments and denoises a single image, eliminating the need for large labeled datasets and improving performance in noisy microscopy images.

Contribution

It combines variational segmentation with self-supervised deep learning to perform joint segmentation and denoising without training data, introducing a novel energy functional and optimization strategy.

Findings

Outperforms sequential methods in noisy microscopy images

Enhances denoising through region-specific learning

Eliminates need for large labeled datasets

Abstract

We develop Self2Seg, a self-supervised method for the joint segmentation and denoising of a single image. To this end, we combine the advantages of variational segmentation with self-supervised deep learning. One major benefit of our method lies in the fact, that in contrast to data-driven methods, where huge amounts of labeled samples are necessary, Self2Seg segments an image into meaningful regions without any training database. Moreover, we demonstrate that self-supervised denoising itself is significantly improved through the region-specific learning of Self2Seg. Therefore, we introduce a novel self-supervised energy functional in which denoising and segmentation are coupled in a way that both tasks benefit from each other. We propose a unified optimisation strategy and numerically show that for noisy microscopy images our proposed joint approach outperforms its sequential counterpart as well as alternative methods focused purely on denoising or segmentation.