Paving the way from in situ plankton image data to a Digital Twin Ocean

Decorative image, HI DIPLO

The field of plankton imaging is rapidly growing. So far, a variety of data processing tools are used for each instrument, specifically tailored to its optical properties. This lack of a consistent cross-platform protocol for data analysis impedes the inter-comparability of instruments, and integration into a harmonized global dataset. Here, DIPLO will develop a novel, efficient and user-friendly data processing pipeline to analyze image data independent of the instrument with which raw data was collected, covering the entire data processing task from raw images to ecologically meaningful data, e.g. segmentation of raw images and classification.

This project will combine a suite of modern approaches from computer vision, artificial intelligence and deep learning (e.g. convolutional neural networks). The result will be a consistent and harmonized cross-platform 4-D plankton image database, which is a critical step towards an image-based ecosystem component of a “Digital Twin Ocean”.


Chobola, T., Müller, G., Dausmann, V., Theileis, A., Taucher, J., Huisken, J., & Peng, T. (2023). LUCYD: A Feature-Driven Richardson-Lucy Deconvolution Network. In H. Greenspan, A. Madabhushi, P. Mousavi, S. Salcudean, J. Duncan, T. Syeda-Mahmood, & R. Taylor (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 (pp. 656–665). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43993-3_63

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