BACKGROUND Cell nuclei segmentation is a fundamental task in microscopy image analysis, based on which multiple biological related analysis can be performed. Although deep learning (DL) based techniques have achieved state-of-the-art performances in image segmentation tasks, these methods are usually complex and require support of powerful computing resources. In addition, it is impractical to allocate advanced computing resources to each dark- or bright-field microscopy, which is widely employed in vast clinical institutions, considering the cost of medical exams. Thus, it is essential to develop accurate DL based segmentation algorithms working with resources-constraint computing. RESULTS An enhanced, light-weighted U-Net (called U-Net+) with modified encoded branch is proposed to potentially work with low-resources computing. Through strictly controlled experiments, the average IOU and precision of U-Net+ predictions are confirmed to outperform other prevalent competing methods with 1.0% to 3.0% gain on the first stage test set of 2018 Kaggle Data Science Bowl cell nuclei segmentation contest with shorter inference time. CONCLUSIONS Our results preliminarily demonstrate the potential of proposed U-Net+ in correctly spotting microscopy cell nuclei with resources-constraint computing.

中文翻译：

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用增强的U-Net进行显微镜细胞核分割。

背景技术细胞核分割是显微镜图像分析中的基本任务，基于此可以进行多种生物学相关分析。尽管基于深度学习（DL）的技术已在图像分割任务中实现了最先进的性能，但是这些方法通常很复杂，并且需要强大的计算资源的支持。另外，考虑到医学检查的成本，将先进的计算资源分配给每个在大范围临床机构中广泛使用的暗场或明场显微镜是不切实际的。因此，开发与资源约束计算一起使用的基于DL的精确分割算法至关重要。结果提出了一种经过改进的轻量级U-Net（称为U-Net +），具有经过修改的编码分支，可潜在地与低资源计算一起使用。通过严格控制的实验，在2018年Kaggle数据科学钵细胞核分割竞赛的第一阶段测试中，U-Net +预测的平均IOU和精度被证实优于其他流行的竞争方法，并获得了1.0％至3.0％的收益，且推理时间更短。结论我们的研究结果初步证明了提出的U-Net +在利用资源约束计算正确定位显微镜细胞核中的潜力。