NuClick：从原子核的点击到原子核的边界
NuClick: From Clicks in the Nuclei to Nuclear Boundaries
摘要：
性能最好的核分割方法是基于深度学习算法，需要大量的注释数据。然而，收集用于核分割的注释是一项非常劳动密集且耗时的任务。因此，提供一种可以促进和加速该过程的工具是非常苛刻的。在这里，我们提出了一个简单而有效的框架，基于卷积神经网络，命名为NuClick，它可以通过接受每个细胞核内的单个点位置（或点击）来精确分割细胞核边界。基于点击的位置，生成包括以这些位置为中心的2D高斯分布的包含图和排除图。这些映射作为网络的引导信号，因为它们被连接到输入图像。包含图聚焦于所需的核，而排除图指示相邻的核，并改善核杂波场景中的分割结果。NuClick不仅有助于从看不见的数据中收集更多的注释，而且还为深度模型提供了出色的分割输出。值得一提的是，在NuClick生成的标签上训练的实例分割模型能够在LYON 19挑战中排名第一。
英文摘要：
Best performing nuclear segmentation methods are based on deep learning algorithms that require a large amount of annotated data. However, collecting annotations for nuclear segmentation is a very labor-intensive and time-consuming task. Thereby, providing a tool that can facilitate and speed up this procedure is very demanding. Here we propose a simple yet efficient framework based on convolutional neural networks, named NuClick, which can precisely segment nuclei boundaries by accepting a single point position (or click) inside each nucleus. Based on the clicked positions, inclusion and exclusion maps are generated which comprise 2D Gaussian distributions centered on those positions. These maps serve as guiding signals for the network as they are concatenated to the input image. The inclusion map focuses on the desired nucleus while the exclusion map indicates neighboring nuclei and improve the results of segmentation in scenes with nuclei clutter. The NuClick not only facilitates collecting more annotation from unseen data but also leads to superior segmentation output for deep models. It is also worth mentioning that an instance segmentation model trained on NuClick generated labels was able to rank first in LYON19 challenge.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
