paper

The TYC Dataset for Understanding Instance-Level Semantics and Motions of Cells in Microstructures

arXiv:2308.12116 · doi:10.1109/ICCVW60793.2023.00426

Abstract

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 dense annotated high-resolution brightfield microscopy images, including about k instance masks. We also release curated video clips composed of 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.

Accepted at ICCV 2023 Workshop on BioImage Computing. Project page (with links to the dataset and code): https://christophreich1996.github.io/tyc_dataset/

References in corpus (6)