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

TL;DR

The TYC dataset provides a large-scale, high-resolution collection of microscopy images and videos with extensive annotations, enabling improved understanding of cell semantics and motions in microstructured environments.

Contribution

We introduce the TYC dataset, the largest annotated microscopy dataset with diverse microstructures and motions, supporting advanced analysis of cells in biomedical research.

Findings

TYC dataset contains 19k instance masks and 1293 high-res images.

It exceeds previous datasets in size, variability, and complexity.

Standardized evaluation strategy facilitates benchmarking.

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 $105$ 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.