本文为美国加州理工学院（作者：Can (Sunny) Cui）的学士论文，共41页。

对单个细胞分辨率下的活体细胞成像实验的分析，为深入了解生物系统的内部工作机制提供了令人兴奋的见解。生物成像和计算机视觉的进步使得自然图像的分割具有高精度。然而，在单细胞分辨率下实现分割流水线的自动化仍然是一项具有挑战性的任务。复杂的深度学习模型需要大量的、注释良好的数据集，而这些数据集在生物学中是很难获得的。在这项研究中，我们探索各种方法来优化最先进的深度学习框架，尽管能够使用的资源有限。我们训练了大量的模型来量化它们的容量，并测量时间信息、空间感知和迁移学习对模型性能的影响。我们发现，虽然训练集的大小对于提高模型的精确度是最有影响的，但是当训练数据稀疏时，我们可以利用空间感知和迁移学习等技术来获得合理的性能。这些见解表明，在数据丰富的情况下，细胞分析中轻量模型的性能可以与它们的重量级模型相同。

Analysis of live-cell imaging experiments at the resolution of single cells provides exciting insights into the inner workings of biological systems. Advances in biological imaging and computer vision allow for segmentation of natural images with a high degree of accuracy. However, automation of the segmentation pipeline at the single cell resolution remains a challenging task. Complex deep learning models require large, well-annotated datasets that are rarely available in biology. In this research, we explore various methods that optimize state of the art deep learning frameworks, despite limited resources. We trained a large permutation of models to quantify their capacity and to measure the effects of temporal information, spatial awareness and transfer learning on model performance. We find that, although training set size is most impactful in improving model accuracy, we can leverage techniques like spatial awareness and transfer learning to obtain reasonable performance when training data is sparse. These insights show that, with an abundance of data, light-weight models can be as performant as their heavy-weight counterparts in cellular analysis.

1.       引言

2. 研究方法

3. 结果

4. 结论

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