Cell instance segmentation is a new and challenging task aiming at joint detection and segmentation of every cell in an image. Recently, many instance segmentation methods have applied in this task. Despite their great success, there still exists two main weaknesses caused by uncertainty of localizing cell center points. First, densely packed cells can easily be recognized into one cell. Second, elongated cell can easily be recognized into two cells. To overcome these two weaknesses, we propose a novel cell instance segmentation network based on multi-scheme regression guidance. With multi-scheme regression guidance, the network has the ability to look each cell in different views. Specifically, we first propose a gaussian guidance attention mechanism to use gaussian labels for guiding the network's attention. We then propose a point-regression module for assisting the regression of cell center. Finally, we utilize the output of the above two modules to further guide the instance segmentation. With multi-scheme regression guidance, we can take full advantage of the characteristics of different regions, especially the central region of the cell. We conduct extensive experiments on benchmark datasets, DSB2018, CA2.5 and SCIS. The encouraging results show that our network achieves SOTA (state-of-the-art) performance. On the DSB2018 and CA2.5, our network surpasses previous methods by 1.2% (AP50). Particularly on SCIS dataset, our network performs stronger by large margin (3.0% higher AP50). Visualization and analysis further prove that our proposed method is interpretable.

翻译：图像中每个单元格的切换分解是一个具有挑战性的新任务, 目的是共同检测和分割图像中的每个单元格。 最近, 许多例分解方法都应用了这一任务。 尽管它们取得了巨大成功, 但仍存在着两个主要的弱点。 首先, 密集包装的单元格很容易被识别成一个单元格。 第二, 宽的单元格很容易被识别成两个单元格。 为了克服这两个弱点, 我们提议了一个基于多系统回归指导的新颖的细胞分解网络。 通过多系统回归指导, 网络有能力从不同的观点中查看每个单元格。 具体地说, 我们首先建议一个粗体指导关注机制, 使用粗体标志来引导网络的注意。 我们然后提出一个点反向模型模块, 协助细胞中心的回归。 最后, 我们利用以上两个模块的输出来进一步指导实例分解。 通过多系统回归指导, 我们可以充分利用不同区域的特点, 特别是该单元格的中央区域。 我们在基准数据集、 DSB2018、 CA2.5 和 SITA 的更强的解析方法上进行广泛的实验。 我们的SO- 显示我们先前的SAS- 的SB 的成绩, 通过SBA 和SLA 的成绩。