Self2Seg: Single-Image Self-Supervised Joint Segmentation and Denoising
This addresses the need for efficient image analysis in microscopy without extensive labeled datasets, though it is incremental in combining existing techniques.
The paper tackled the problem of joint segmentation and denoising from a single image without labeled data, resulting in a method that outperforms sequential and alternative approaches for noisy microscopy images.
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.