Summary of Open Source Infrastructure For Automatic Cell Segmentation, by Aaron Rock Menezes and Bharath Ramsundar
Open Source Infrastructure for Automatic Cell Segmentation
by Aaron Rock Menezes, Bharath Ramsundar
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Quantitative Methods (q-bio.QM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper presents an open-source infrastructure for automated cell segmentation using the UNet model, a deep-learning architecture effective in image segmentation tasks. This implementation integrates into the DeepChem package, enhancing accessibility and usability for researchers and practitioners. The resulting tool offers a convenient and user-friendly interface, reducing the barrier to entry while maintaining high accuracy. Benchmarked against various datasets, this model demonstrates robustness and versatility across different imaging conditions and cell types. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Automated cell segmentation is important for many biological and medical applications, such as counting cells, analyzing their shapes, and discovering new drugs. Right now, people have to manually segment cells, which takes a long time and can be subjective. That’s why we need robust automated methods. This paper presents an open-source tool that uses the UNet model, a deep-learning architecture that is good at image segmentation tasks. This implementation integrates into the DeepChem package, making it easy for researchers and practitioners to use. The tool has a simple and user-friendly interface, making it easier for people to get started while still achieving high accuracy. We tested this model on many different datasets and showed how well it works in different imaging conditions and with different types of cells. |
Keywords
» Artificial intelligence » Deep learning » Image segmentation » Unet
