This topic invites original research and rigorous advances at the interface of computation and biological imaging, aligned with the mission of the Computational BioImaging section of Frontiers in Bioinformatics. We seek contributions that develop, validate, or benchmark novel computational methods, software tools, and algorithms that drive discovery from cellular to organ scales—bridging innovative computation with biological insight.
We welcome work on: - Computer vision and machine learning for bioimage understanding, including foundation models, self-/weakly supervised learning, and interpretable AI.
- Computational optics and algorithm–hardware co-design, with emphasis on real-time and in situ analysis.
- Hardware-accelerated image analysis (GPU/TPU/FPGA), streaming pipelines, and low-latency inference at scale.
- Image acquisition and storage strategies that optimize data quality, throughput, and FAIR accessibility.
- Image restoration, registration, segmentation, and tracking, with attention to uncertainty, generalization, and reproducibility.
- Multidimensional and multiparametric analysis across space, time, spectrum, and modality (e.g., 3D/4D/5D, multimodal, single-molecule to whole-organ).
- Multiscale methods linking molecular/cellular phenotypes to tissue and organ-level organization, including graph- and topology-based approaches.
- Open-source software development, standards, and benchmarks that enable transparency, reuse, and community adoption.
- Scalable workflows for big image data annotation, curation, sharing, and lifecycle management, including privacy-preserving and federated paradigms.
Submissions should provide comprehensive methodological contributions or robust applications that clearly advance computational capabilities for biological imaging. High-value studies include algorithmic innovation, principled evaluation on representative datasets, reproducible software artifacts, and integration with experimental or clinical workflows. Work that applies routine or conventional computational techniques without substantive methodological advancement or generalizable insight falls outside the scope of this topic.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Case Report
Data Report
Editorial
FAIR² Data
General Commentary
Hypothesis and Theory
Methods
Mini Review
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Case Report
Data Report
Editorial
FAIR² Data
General Commentary
Hypothesis and Theory
Methods
Mini Review
Opinion
Original Research
Perspective
Review
Systematic Review
Technology and Code
Keywords: Computational bioimaging, bioimage analysis, machine learning for imaging, interpretable AI, computational optics, hardware-accelerated inference, image restoration and segmentation, multimodal and multidimensional imaging, multiscale phenotyping
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
