Image processing articles from across Nature Portfolio

Cell segmentation currently involves the use of various bespoke algorithms designed for specific cell types, tissues, staining methods and microscopy technologies. We present a universal algorithm that can segment all kinds of microscopy images and cell types across diverse imaging protocols.

Improved imaging techniques are required to help advance our understanding of the complex role of the tumour microenvironment (TME). Here, the authors develop a high-throughput, highly multiplexed tissue visualisation workflow and demonstrate its utility by characterising the response of the TME to radiotherapy in preclinical models of glioblastoma.

A pretrained foundation model (UniFMIR) enables versatile and generalizable performance across diverse fluorescence microscopy image reconstruction tasks.

Cell segmentation currently involves the use of various bespoke algorithms designed for specific cell types, tissues, staining methods and microscopy technologies. We present a universal algorithm that can segment all kinds of microscopy images and cell types across diverse imaging protocols.

When it comes to bioimaging and image analysis, details matter. Papers in this issue offer guidance for improved robustness and reproducibility.

The visualization and analysis of biological events using fluorescence microscopy is limited by the noise inherent in the images obtained. Now, a self-supervised spatial redundancy denoising transformer is proposed to address this challenge.

New twists on established methods and multimodal imaging are poised to bridge gaps between cellular and organismal imaging.