Insights in Computational BioImaging

Imaging is central to modern biology, enabling the study of structure, dynamics and function across molecular, cellular, tissue and organismal scales. Advances in super-resolution and light-sheet microscopy, cryo-electron tomography, spatial imaging and whole-slide pathology are producing increasingly large and complex datasets. Computational bioimaging is essential for transforming these data into quantitative and reproducible biological insight, yet persistent challenges remain in segmentation, feature extraction, registration, tracking and cross-modal analysis. Recent developments in deep learning, self-supervised learning, foundation models and physics-informed approaches have expanded image-analysis capabilities, while also exposing concerns related to data quality, interpretability, generalizability, benchmarking and reproducibility. Robust computational frameworks that perform reliably across imaging platforms, biological systems and research settings are therefore still needed.This Insights in collection aims to examine recent advances in computational bioimaging and their capacity to connect image-derived measurements with meaningful biological phenotypes. It will explore the development, validation and application of methods for image segmentation, classification, restoration, registration, tracking, modelling and quantitative analysis. It will also consider the methodological, computational and data-related barriers that limit the adoption of these approaches, including limited training data, domain shifts, inconsistent evaluation practices and challenges in biological interpretation. Particular attention will be given to how open resources, standardized benchmarks, uncertainty estimation and reproducible workflows can strengthen the reliability and translational potential of computational bioimaging.To gather further insights into computational methods for imaging data across molecular, cellular, tissue and organismal scales, this Insights in collection welcomes contributions addressing, but not limited to, the following themes:• Deep learning, self-supervised learning and foundation models for bioimage analysis• Image segmentation, classification, restoration, denoising and reconstruction• Bioimage registration, tracking, modelling and quantitative phenotyping• Computational analysis of microscopy, super-resolution and light-sheet imaging data• Spatial, multiplexed and single-cell imaging in tissue contexts• Cryo-electron microscopy and cryo-electron tomography image processing• Multimodal and cross-scale computational imaging• Open, reproducible and FAIR bioimage informatics tools and workflows• Benchmark datasets, evaluation frameworks and standards for bioimage analysis• Interpretable, generalizable and uncertainty-aware computational methodsThis collection welcomes contributions reflecting on current developments and plotting pathways for upcoming research, from Editorial Board Members of the section, from authors they refer, and, on invitation, from a small number of external researchers whose work is shaping the direction of the field. Authors are encouraged to engage critically with their fields, identify the current challenges and propose novel solutions that advance bioinformatics tools and their application to real biological problems. Topic editors: Luca Mesin, Xiangxue Wang, Omar M Makram Source: https://www.frontiersin.org/research-topics/84764/insights-in-computational-bioimaging