Project Details
Projekt
Digital diatom analysis: investigating advanced deep learning-based approaches for gigapixel-sized virtual slides
Subject Area
Ecology and Biodiversity of Plants and Ecosystems
Term
since 2021
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 463395318
Diatoms are a speciose group of microalgae playing important ecological roles in a broad range of aquatic habitats. Light microscopic investigation of their silicate shells is one of the oldest, but still widely used approaches to determining their assemblage composition in ecological, paleo-ecological and applied research. A transition of these methods to digital counterparts has been ongoing for some time now, with abundant indications for improvements related to consistency, transparency, precision and statistical power in ecological / morphometric studies. Thus far, such digital diatom analyses have mostly targeted only one or a few taxa, or required substantial manual annotation effort for taxonomic identification in the case of complex communities. Pilot studies have, however, now made it clear that deep convolutional networks will probably soon be able to also enable digital diatom analysis to work in “real life” conditions, with species rich communities. We identified three main problem fields presently hindering this, related to 1) segmentation of diatom images in spite of complex backgrounds (caused by sediment and other particles); 2) efficiently increasing taxonomic coverage of training data sets in the face of concave rank-abundance relationships in natural communities; and 3) the relative importance of size, outline shape and texture for algorithmic diatom identification. We would like to address these problems by a series of deep learning experiments and pave the way for a routine application of digital diatom analysis supported by deep learning models in community ecology, paleoecology and other types of diatom investigations.
DFG Programme
Research Grants
