An Instance Segmentation Dataset of Yeast Cells in Microstructures
This dataset aims to facilitate the development of new cell segmentation approaches for researchers in microscopy and bioimage analysis, but it is incremental as it primarily offers a new resource rather than a methodological breakthrough.
The paper introduces a new dataset for segmenting yeast cells in microstructures, providing pixel-wise instance segmentation labels for 493 microscopy images to address challenges in extracting single-cell information from complex environments.
Extracting single-cell information from microscopy data requires accurate instance-wise segmentations. Obtaining pixel-wise segmentations from microscopy imagery remains a challenging task, especially with the added complexity of microstructured environments. This paper presents a novel dataset for segmenting yeast cells in microstructures. We offer pixel-wise instance segmentation labels for both cells and trap microstructures. In total, we release 493 densely annotated microscopy images. To facilitate a unified comparison between novel segmentation algorithms, we propose a standardized evaluation strategy for our dataset. The aim of the dataset and evaluation strategy is to facilitate the development of new cell segmentation approaches. The dataset is publicly available at https://christophreich1996.github.io/yeast_in_microstructures_dataset/ .