Volume II: Methods, Tools and Algorithms in Computational Bioimaging

Building on the success of our inaugural collection, this second volume continues to advance the frontier of computational biological imaging. We invite original research and rigorous methodological contributions at the intersection of computation and biological imaging, in alignment with the Computational BioImaging section of Frontiers in Bioinformatics. Our goal remains the same: to accelerate discovery from subcellular to whole-organ scales by uniting innovative algorithms, scalable tools, and quantitative insight.We welcome contributions across the following themes:o Computer vision and machine learning for bioimage understanding — including large foundation models, self- and weakly supervised learning, and interpretable or explainable AI tailored to biological contexts.o Computational optics and algorithm–hardware co-design — with a focus on real-time, in situ, and adaptive imaging analysis pipelines.o Hardware-accelerated image analysis — leveraging GPU, TPU, and FPGA architectures for streaming pipelines and low-latency inference at scale.o Image acquisition and data management — strategies that optimize data quality, throughput, and FAIR accessibility from capture to archival.o Image restoration, registration, segmentation, and tracking — with emphasis on uncertainty quantification, cross-domain generalization, and reproducibility.o Multidimensional and multiparametric analysis — spanning space, time, spectrum, and modality (3D/4D/5D, multimodal, single-molecule to whole-organ).o Multiscale and topology-aware methods — linking molecular and cellular phenotypes to tissue and organ-level organization through graph-, network-, and topology-based frameworks.o Open-source tools, standards, and community benchmarks — enabling transparency, interoperability, and broad community adoption.o Scalable workflows for big image data — covering annotation, curation, sharing, and lifecycle management, including privacy-preserving and federated learning paradigms.Submissions should present substantive methodological advances or robust applications that meaningfully expand computational capabilities for biological imaging. High-value contributions include algorithmic innovation, principled benchmarking on representative datasets, reproducible software artifacts, and integration with experimental or clinical workflows. Studies applying established or conventional computational techniques without significant methodological novelty or transferable insight fall outside the scope of this volume. Topic editors: Felix Yuran Zhou, Ayodeji Olalekan Salau, Enock Adjei Agyekum, Mansoor Hayat, Letu Qingge, German Corredor Source: https://www.frontiersin.org/research-topics/83860/volume-ii-methods-tools-and-algorithms-in-computational-bioimaging