# An Instance Segmentation Dataset of Yeast Cells in Microstructures

arXiv: https://arxiv.org/abs/2304.07597 · HTML: https://mlsanity.com/paper/2304.07597/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 15 Apr 2023.

## Builds on (most cited first, 13 of 13)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [Video Instance Segmentation](https://mlsanity.com/paper/1905.04804/) (2019)
- [Cell Detection with Star-convex Polygons](https://mlsanity.com/paper/1806.03535/) (2018)
- [Attention-Based Transformers for Instance Segmentation of Cells in Microstructures](https://mlsanity.com/paper/2011.09763/) (2020)
- [OSS-Net: Memory Efficient High Resolution Semantic Segmentation of 3D Medical Data](https://mlsanity.com/paper/2110.10640/) (2021)
- [Multiclass Yeast Segmentation in Microstructured Environments with Deep Learning](https://mlsanity.com/paper/2011.08062/) (2020)
- [Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy](https://mlsanity.com/paper/2106.08285/) (2021)
- [Maximizing Information Gain for the Characterization of Biomolecular Circuits](https://mlsanity.com/paper/2101.02924/) (2021)
- [Analysis of the performance of U-Net neural networks for the segmentation of living cells](https://mlsanity.com/paper/2210.01538/) (2022)

## Built on by (most cited first, 3 of 3)

- [The TYC Dataset for Understanding Instance-Level Semantics and Motions of Cells in Microstructures](https://mlsanity.com/paper/2308.12116/) (2023)
- [Practical Guidelines for Cell Segmentation Models Under Optical Aberrations in Microscopy](https://mlsanity.com/paper/2404.08549/) (2024)
- [A Perspective on Deep Vision Performance with Standard Image and Video Codecs](https://mlsanity.com/paper/2404.12330/) (2024)

## Abstract (arXiv)

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/ .