An Instance Segmentation Dataset of Yeast Cells in Microstructures
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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/ .
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 13 of its 28 references that are in the map. First posted on arXiv 15 Apr 2023.
Builds on
13 of 28 references.
References · 13
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Citing papers per year: 2023: 1; 2024: 2.
3 papers.
- 2023The TYC Dataset for Understanding Instance-Level Semantics and Motions of Cells in Microstructurescited by 2
- 2024Practical Guidelines for Cell Segmentation Models Under Optical Aberrations in Microscopycited by 1
- 2024A Perspective on Deep Vision Performance with Standard Image and Video Codecscited by 1
- 2024A Perspective on Deep Vision Performance with Standard Image and Video Codecs18 Apr 2024
- 2024Practical Guidelines for Cell Segmentation Models Under Optical Aberrations in Microscopy12 Apr 2024
- 2023The TYC Dataset for Understanding Instance-Level Semantics and Motions of Cells in Microstructures23 Aug 2023