基于UNet的开源细胞分割工具，提升生物医学研究效率。
Open Source Infrastructure for Automatic Cell Segmentation
- 采用UNet深度学习模型实现自动细胞分割
- 在多个数据集上验证，适应不同成像条件和细胞类型
- 集成至DeepChem，降低使用门槛，适合科研人员快速上手
自动化细胞分割对生物医学应用至关重要，可支持细胞计数、形态分析和药物发现等任务。然而手动分割耗时且主观性强，亟需高效可靠的自动化方法。本文提出一套开源基础设施，基于在图像分割任务中表现优异的UNet深度学习架构，集成于开源DeepChem工具包中，显著提升研究人员和实践者的使用便捷性。该工具提供直观易用的界面，同时保持高精度。我们还在多个数据集上对该模型进行了基准测试，证明其在不同成像条件和细胞类型下具有鲁棒性和泛化能力。
原文摘要 · Abstract (English)
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.
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