Open Source Infrastructure for Automatic Cell Segmentation
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929498940768256 |
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| author | Menezes, Aaron Rock Ramsundar, Bharath |
| author_facet | Menezes, Aaron Rock Ramsundar, Bharath |
| contents | Automated cell segmentation is crucial for various biological and medical applications, facilitating tasks like cell counting, morphology analysis, and drug discovery. However, manual segmentation is time-consuming and prone to subjectivity, necessitating robust automated methods. This paper presents open-source infrastructure, utilizing the UNet model, a deep-learning architecture noted for its effectiveness in image segmentation tasks. This implementation is integrated into the open-source 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 for cell segmentation while maintaining high accuracy. Additionally, we benchmark this model against various datasets, demonstrating its robustness and versatility across different imaging conditions and cell types. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08163 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Open Source Infrastructure for Automatic Cell Segmentation Menezes, Aaron Rock Ramsundar, Bharath Machine Learning Computer Vision and Pattern Recognition Quantitative Methods Automated cell segmentation is crucial for various biological and medical applications, facilitating tasks like cell counting, morphology analysis, and drug discovery. However, manual segmentation is time-consuming and prone to subjectivity, necessitating robust automated methods. This paper presents open-source infrastructure, utilizing the UNet model, a deep-learning architecture noted for its effectiveness in image segmentation tasks. This implementation is integrated into the open-source 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 for cell segmentation while maintaining high accuracy. Additionally, we benchmark this model against various datasets, demonstrating its robustness and versatility across different imaging conditions and cell types. |
| title | Open Source Infrastructure for Automatic Cell Segmentation |
| topic | Machine Learning Computer Vision and Pattern Recognition Quantitative Methods |
| url | https://arxiv.org/abs/2409.08163 |