Deep learning is rapidly becoming the technique of choice for automated segmentation of nuclei in biological image analysis workflows. In order to evaluate the feasibility of training nuclear segmentation models on small, custom annotated image datasets that have been augmented, we have designed a computational pipeline to systematically compare different nuclear segmentation model architectures and model training strategies. Using this approach, we demonstrate that transfer learning and tuning of training parameters, such as the composition, size and pre-processing of the training image dataset, can lead to robust nuclear segmentation models, which match, and often exceed, the performance of existing, off-the-shelf deep learning models pre-trained on large image datasets. We envision a practical scenario where deep learning nuclear segmentation models trained in this way can be shared across a laboratory, facility, or institution, and continuously improved by training them on progressively larger and varied image datasets. Our work provides computational tools and a practical framework for deep learning-based biological image segmentation using small annotated image datasets.

中文翻译：

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用于核分割的深度学习管道

深度学习正迅速成为在生物图像分析工作流程中自动分割核的首选技术。为了评估在增强的小型自定义带注释图像数据集上训练核分割模型的可行性，我们设计了一条计算管道来系统地比较不同的核分割模型架构和模型训练策略。使用这种方法，我们证明了转移学习和训练参数的调整（例如训练图像数据集的组成，大小和预处理）可以导致强大的核分割模型，该模型可以匹配并经常超过现有的性能，在大型图像数据集上预先训练的现成的深度学习模型。我们设想了一个实际场景，其中以这种方式训练的深度学习核分割模型可以在实验室，设施或机构之间共享，并通过在逐渐扩大和变化的图像数据集上进行训练来不断改进。我们的工作提供了使用小的带注释的图像数据集的基于深度学习的生物图像分割的计算工具和实用框架。