About this Research Topic
Recent technological advances in microscopy have pushed the boundaries of the acquisition systems, especially in terms of image resolution and speed of acquisition, which, in turn, has boosted research in diverse fields such as medicine, biology, geology, materials science, etc. The enhanced speed and resolution produce a rapidly growing body of acquired images, creating an ever-increasing need for automated and robust image processing and analysis methods and the adoption of “Big Data” approaches. As a response, there is increased interest in novel theoretical and applied methodologies to enable the analysis and quantification of image data produced by high-throughput microscopes.
We call for contributions presenting novel methods and applications of image processing and computer vision for microscopy data. Potential contributions include, but are not limited to:
● Image processing methods and applications for automated analysis of Big microscopy Data, including phenotyping, diagnosis, atlasing, rare event detection, tracking, shape analysis, and spatial analysis.
● Computer vision algorithms and applications for the analysis of multi-modal (2D, 3D, 4D, and/or multiple-channel) microscopy image data.
● Machine learning methods and applications for the reconstruction, classification, detection, registration or dense segmentation of images obtained by any microscopy modality.
● Development of image processing software and platforms for the storage, annotation, indexation, management and visualization of Big microscopy Data.
● Description and evaluation of new publicly available microscopy image datasets and databases for algorithm comparisons.
Keywords: Microscopy, Image Processing, Computer Vision, Machine Learning, Artificial Intelligence
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.