mlsanityData to 7 Oct 2026

An Instance Segmentation Dataset of Yeast Cells in Microstructures

arXiv 2304.07597 ↗First posted 15 Apr 2023cs.CVcited by 3

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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.

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13 of 28 references.

References · 13

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  8. 2020Attention-Based Transformers for Instance Segmentation of Cells in Microstructurescited by 38
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  10. 2020Multiclass Yeast Segmentation in Microstructured Environments with Deep Learningcited by 7
  11. 2021Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopycited by 4
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1 more
  1. 2022Analysis of the performance of U-Net neural networks for the segmentation of living cellscited by 2

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Citing papers per year: 2023: 1; 2024: 2.

3 papers.