The TYC Dataset for Understanding Instance-Level Semantics and Motions
  of Cells in Microstructures
Christoph Reich Tim Prangemeier Heinz Koeppl
Segmenting cells and tracking their motion over time is a common task in
biomedical applications. However, predicting accurate instance-wise
segmentation and cell motions from microscopy imagery remains a challenging
task. Using microstructured environments for analyzing single cells in a
constant flow of media adds additional complexity. While large-scale labeled
microscopy datasets are available, we are not aware of any large-scale dataset,
including both cells and microstructures. In this paper, we introduce the
trapped yeast cell (TYC) dataset, a novel dataset for understanding
instance-level semantics and motions of cells in microstructures. We release
$105$ dense annotated high-resolution brightfield microscopy images, including
about $19$k instance masks. We also release $261$ curated video clips composed
of $1293$ high-resolution microscopy images to facilitate unsupervised
understanding of cell motions and morphology. TYC offers ten times more
instance annotations than the previously largest dataset, including cells and
microstructures. Our effort also exceeds previous attempts in terms of
microstructure variability, resolution, complexity, and capturing device
(microscopy) variability. We facilitate a unified comparison on our novel
dataset by introducing a standardized evaluation strategy. TYC and evaluation
code are publicly available under CC BY 4.0 license.