With the continuous expansion of single cell biology, the observation of the behaviour of individual cells over extended durations and with high accuracy has become a problem of central importance. Surprisingly, even for yeast cells that have relatively regular shapes, no solution has been proposed that reaches the high quality required for long-term experiments for segmentation and tracking (S&T) based on brightfield images. Here, we present CellStar, a tool chain designed to achieve good performance in long-term experiments. The key features are the use of a new variant of parametrized active rays for segmentation, a neighbourhood-preserving criterion for tracking, and the use of an iterative approach that incrementally improves S&T quality. A graphical user interface enables manual corrections of S&T errors and their use for the automated correction of other, related errors and for parameter learning. We created a benchmark dataset with manually analysed images and compared CellStar with six other tools, showing its high performance, notably in long-term tracking. As a community effort, we set up a website, the Yeast Image Toolkit, with the benchmark and the Evaluation Platform to gather this and additional information provided by others.

Zero anaphora is an element of the coreference resolution task that has not yet been directly addressed in Polish and, in most studies, it has been left as the most challenging aspect for further investigation. This article presents an initial study of this problem. The preparation of a machine learning approach, alongside engineering features based on linguistic study of the KPWr corpus, is discussed. This study utilizes existing tools for Polish coreference resolution as sources of partial coreferential clusters containing pronoun, noun and named entity mentions. They are also used as baseline zero coreference resolution systems for comparison with our system. The evaluation process is focused not only on clustering correctness, without taking into account types of mentions, using standard CoNLL-2012 measures, but also on the informativeness of the resulting relations. According to the annotation approach used for coreference to the KPWr corpus, only named entities are treated as mentions that are informative enough to constitute a link to real world objects. Consequently, we provide an evaluation of informativeness based on found links between zero anaphoras and named entities. For the same reason, we restrict coreference resolution in this study to mention clusters built around named entities.

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