Engineering Research Center of Learning-Based Intelligent System (Ministry of Education), The Key Laboratory of Computer Vision and System (Ministry of Education), and the School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, China.
School of Medical Laboratory, Tianjin Medical University, Tianjin 300204, China.
Bioinformatics. 2022 Jun 24;38(Suppl 1):i53-i59. doi: 10.1093/bioinformatics/btac219.
The presence of tumor cell clusters in pleural effusion may be a signal of cancer metastasis. The instance segmentation of single cell from cell clusters plays a pivotal role in cluster cell analysis. However, current cell segmentation methods perform poorly for cluster cells due to the overlapping/touching characters of clusters, multiple instance properties of cells, and the poor generalization ability of the models.
In this article, we propose a contour constraint instance segmentation framework (CC framework) for cluster cells based on a cluster cell combination enhancement module. The framework can accurately locate each instance from cluster cells and realize high-precision contour segmentation under a few samples. Specifically, we propose the contour attention constraint module to alleviate over- and under-segmentation among individual cell-instance boundaries. In addition, to evaluate the framework, we construct a pleural effusion cluster cell dataset including 197 high-quality samples. The quantitative results show that the numeric result of APmask is > 90%, a more than 10% increase compared with state-of-the-art semantic segmentation algorithms. From the qualitative results, we can observe that our method rarely has segmentation errors.
胸腔积液中肿瘤细胞簇的存在可能是癌症转移的信号。从细胞簇中单细胞的实例分割在簇细胞分析中起着关键作用。然而，由于细胞簇的重叠/接触特征、细胞的多个实例属性以及模型的较差泛化能力，当前的细胞分割方法在簇细胞上的性能较差。
在本文中，我们提出了一种基于细胞簇组合增强模块的用于簇细胞的轮廓约束实例分割框架（CC 框架）。该框架可以从细胞簇中准确地定位每个实例，并在少量样本下实现高精度的轮廓分割。具体来说，我们提出了轮廓注意约束模块，以减轻单个细胞实例边界的过分割和欠分割。此外，为了评估该框架，我们构建了一个包含 197 个高质量样本的胸腔积液簇细胞数据集。定量结果表明，APmask 的数值结果>90%，比最先进的语义分割算法提高了 10%以上。从定性结果来看，我们可以观察到我们的方法很少出现分割错误。
