论文标题
Hunis：高性能无监督的核实例分割
HUNIS: High-Performance Unsupervised Nuclei Instance Segmentation
论文作者
论文摘要
在这项工作中提出了高性能无监督的核实例分割（HUNIS）方法。 Hunis由两阶段的障碍行动组成。第一个阶段包括：1）像素强度的自适应阈值，2）纳入核大小/形状先验和3）删除假阳性核实例。然后，Hunis通过收到第一阶段的指导来进行第二阶段的细分。第二阶段利用在第一阶段获得的分割掩模，并利用颜色和形状分布来进行更精确的分割。两阶段设计的主要目的是从第一阶段到第二阶段提供像素伪标签。这种自学机制是新颖有效的。 MONUSEG数据集的实验结果表明，Hunis的表现优于所有其他无监督方法。它在最先进的监督方法中也具有竞争力。
A high-performance unsupervised nuclei instance segmentation (HUNIS) method is proposed in this work. HUNIS consists of two-stage block-wise operations. The first stage includes: 1) adaptive thresholding of pixel intensities, 2) incorporation of nuclei size/shape priors and 3) removal of false positive nuclei instances. Then, HUNIS conducts the second stage segmentation by receiving guidance from the first one. The second stage exploits the segmentation masks obtained in the first stage and leverages color and shape distributions for a more accurate segmentation. The main purpose of the two-stage design is to provide pixel-wise pseudo-labels from the first to the second stage. This self-supervision mechanism is novel and effective. Experimental results on the MoNuSeg dataset show that HUNIS outperforms all other unsupervised methods by a substantial margin. It also has a competitive standing among state-of-the-art supervised methods.
