As bioimaging has rapidly become a key technology in modern life sciences, the need for providing life scientists with comprehensive quantitative measurements of biological systems has materialized. In the past decades, new computational methods have been adopted at all steps involved in imaging, from the raw acquisition to all kind of post-processing. Bioimage analysis has therefore become a prominent step, essential for leveraging bias induced by manual process and analysis. From the time of basic pixel-based thresholding, the field has followed a fast evolution towards more complex, automated and robust analysis methods.
In parallel, innovation in microscopy technology occurs at a vertiginous pace, with new imaging systems allowing observing biological systems at a level of spatial and temporal resolution never achieved before. Their adoption by life scientists raises a couple of technology and accessibility challenges. First, the gain in resolution usually comes at the expense of producing an exponential amount of complex data. There is therefore a dire need to develop new computational methods that are able to cope with this complexity, as well as with the particularities of the acquired biological systems. Unfortunately, even when a technological solution exists, its accessibility is often at best difficult for the end user. The reasons behind this are multiple and can be related to a lack of explanations in scientific publications or a poor packaging of the method that therefore requires computational expertise to be used. This results in the use of new computational methods by life scientists being a bottleneck, hampering new important biological discoveries.
We call for contributions presenting novel bioimage analysis methods as well as practical implementation of existing ones (through workflows or software platforms) that would facilitate their usage by life scientists. Potential contributions include, but are not limited to:
• Image processing methods and applications for automated analysis of nD biological images acquired by any microscopy modality.
• Computer vision algorithms and machine learning methods for image formation, restoration and reconstruction, shape analysis, statistical modeling, etc.
• Generic algorithms as well as dedicated methods developed for quantifying specific features of a biological system.
• Highly practical implementation of existing methods to new biological models. For those, a proper packaging as workflows or software platforms is required to ensure a broad dissemination to life scientists.
As bioimaging has rapidly become a key technology in modern life sciences, the need for providing life scientists with comprehensive quantitative measurements of biological systems has materialized. In the past decades, new computational methods have been adopted at all steps involved in imaging, from the raw acquisition to all kind of post-processing. Bioimage analysis has therefore become a prominent step, essential for leveraging bias induced by manual process and analysis. From the time of basic pixel-based thresholding, the field has followed a fast evolution towards more complex, automated and robust analysis methods.
In parallel, innovation in microscopy technology occurs at a vertiginous pace, with new imaging systems allowing observing biological systems at a level of spatial and temporal resolution never achieved before. Their adoption by life scientists raises a couple of technology and accessibility challenges. First, the gain in resolution usually comes at the expense of producing an exponential amount of complex data. There is therefore a dire need to develop new computational methods that are able to cope with this complexity, as well as with the particularities of the acquired biological systems. Unfortunately, even when a technological solution exists, its accessibility is often at best difficult for the end user. The reasons behind this are multiple and can be related to a lack of explanations in scientific publications or a poor packaging of the method that therefore requires computational expertise to be used. This results in the use of new computational methods by life scientists being a bottleneck, hampering new important biological discoveries.
We call for contributions presenting novel bioimage analysis methods as well as practical implementation of existing ones (through workflows or software platforms) that would facilitate their usage by life scientists. Potential contributions include, but are not limited to:
• Image processing methods and applications for automated analysis of nD biological images acquired by any microscopy modality.
• Computer vision algorithms and machine learning methods for image formation, restoration and reconstruction, shape analysis, statistical modeling, etc.
• Generic algorithms as well as dedicated methods developed for quantifying specific features of a biological system.
• Highly practical implementation of existing methods to new biological models. For those, a proper packaging as workflows or software platforms is required to ensure a broad dissemination to life scientists.
