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An international team of researchers has developed , an open-code artificial intelligence platform that facilitates the analysis of biomedical images using deep learning techniques. The work has been in Nature Methods.

The team was led by Ignacio Arganda (University of the Basque Country—UPV/EHU, Ikerbasque, Donostia International Â鶹ÒùÔºics Center, and the Biofisika Institute) and Arrate Muñoz-Barrutia (University of Madrid Carlos III, Gregorio Marañón Health Research Institute).

Used to study cellular structures, tissue, and organs across a range of disciplines, image analysis is an essential tool in biomedicine. However, applying AI to analyze these images has traditionally been the preserve of experts in programming and data science. BiaPy breaks down that barrier by offering an easy-to-use platform that allows advanced AI models to be applied without the need for specialized technical knowledge.

"BiaPy aims to democratize access to artificial intelligence in bioimaging by enabling more scientists and health care professionals to harness its potential without the need for advanced programming or machine learning skills," explained Daniel Franco, lead author of the study and currently a postdoctoral researcher at the MRC Laboratory of Molecular Biology and Cambridge University (United Kingdom).

BiaPy allows different types of analysis to be performed on scientific images, such as automatically identifying cells or other biological structures, counting elements, classifying samples according to their appearance, or improving image quality to see the finer details. All this can be done with two-dimensional images as well as with three-dimensional images obtained by means of various microscopy techniques.

In addition, BiaPy has been designed to be efficient and scalable: it can work with a broad variety of data volumes, from a few small images to terabytes of information, such as those generated when tissue or entire organs are scanned.

The tool is based on the use of AI models, which are algorithms trained to recognize patterns in images, similar to the way the human eye can identify shapes or colors. Examples are used to create a model: for example, images in which cells have already been tagged manually. With sufficient training, the model learns to perform these tasks automatically, even on new images it has never seen before.

"BiaPy has also been integrated into the BioImage Model Zoo (bioimage.io), a database in which researchers from around the world share pre-trained models. Thanks to this integration, BiaPy users can reuse existing models for new images or train their own models easily," explained Arrate Muñoz, senior co-author of the paper and member of the European consortium AI4Life that developed the BioImage Model Zoo.

This tool is already being used in advanced scientific projects. One example is CartoCell, a software solution developed in collaboration with the lab coordinated by Luis M. Escudero (Institute of Biomedicine of Seville [Virgen del Rocío University Hospital/CSIC/University of Seville]). CartoCell analyzes microscopy images to reveal hidden patterns in the shape and distribution of cells within 3D epithelial tissue from different organisms.

Another case worthy of note is its application in collaboration with the laboratories of Emmanuel Beaurepaire (École Polytechnique, France) and Jean Livet (Institut de la Vision, Paris). These groups have developed the ChroMS microscopy technique, which allows huge three-dimensional images of entire brains to be obtained using fluorescent colors generated by proteins from jellyfish and corals.

BiaPy is used to automatically detect each cell in these large-scale images, even in densely populated areas of the brain, allowing brain development to be studied by reconstructing the lineage of cells based on their colors and three-dimensional positions.

As an open-access tool, BiaPy is available free of charge to the scientific community, thereby promoting collaboration and the ongoing improvement of the software. It can be used on PCs or servers with multiple graphics cards, as well as in the cloud. It is easy to install and ensures that experiments can be easily repeated in various environments, thus promoting open, reproducible science.

As Arganda, the senior author of the paper, pointed out, "The development of BiaPy represents an important step towards the democratization of advanced artificial computer vision in microscopy. Its accessible design and focus on open collaboration reduce technical barriers, making it easier for more researchers and health care professionals to apply artificial vision to their studies.

"Its compatibility with various computing environments and its open-code nature mean that it is a platform that offers huge potential in driving forward innovation and speeding up scientific discovery."