Learning accurate plant cell segmentation and curation, with minimal human effort

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A fast and reliable segmentation of plant cells is a prerequisite for much of the quantitative work pursued by the collaborators of FOR2581. In the last funding period, we have successfully developed methods – a combination of “deep learning” and “structured prediction” – that allow segmenting plant cells in volumetric light microscopy data with very high accuracy. Unfortunately, these methods require a lot of training data to work reliably. While humans excel in the analysis of natural 2D or 2.5D images, they have no innate ability to efficiently analyze or process truly 3D image data. As a consequence and given the latest advances on the segmentation task itself, the main bottleneck of volumetric image analysis has shifted to the creation of 3D ground truth for training, and to curation of the algorithm results by a human expert.In response, this project has two principal aims: first, to further develop our segmentation tools such that they can be trained using minimal annotations only; and secondly, to develop machine learning methods that can spot and, if possible, automatically correct likely segmentation errors, also trained with minimal human annotation. We will combine our developments in a single generic pipeline that will deliver highly accurate segmentations from multiple sources of weak, easily provided supervision. Just as in the first funding period, the new methods and ideas will be constantly confronted with real data from the research unit to establish virtues and limitations. The best-performing methods will be passed to central project Z02 which will turn research code into open source end-user tools for RU collaboration partners and the bioimaging community at large.