Mo Yapeng, Chen Lijiang, Zhang Lingfeng, Zhao Qi
Institute of Electronic Information Engineering, Beihang University, 37 Xueyuan Road, Haidian District, Beijing 100191, China.
Bioengineering (Basel). 2025 Jan 17;12(1):85. doi: 10.3390/bioengineering12010085.
Due to the labor-intensive manual annotations for nuclei segmentation, point-supervised segmentation based on nuclei coordinate supervision has gained recognition in recent years. Despite great progress, two challenges hinder the performance of weakly supervised nuclei segmentation methods: (1) The stable and effective segmentation of adjacent cell nuclei remains an unresolved challenge. (2) Existing approaches rely solely on initial pseudo-labels generated from point annotations for training, and inaccurate labels may lead the model to assimilate a considerable amount of noise information, thereby diminishing performance. To address these issues, we propose a method based on center-point prediction and pseudo-label updating for precise nuclei segmentation. First, we devise a Gaussian kernel mechanism that employs multi-scale Gaussian masks for multi-branch center-point prediction. The generated center points are utilized by the segmentation module to facilitate the effective separation of adjacent nuclei. Next, we introduce a point-guided attention mechanism that concentrates the segmentation module's attention around authentic point labels, reducing the noise impact caused by pseudo-labels. Finally, a label updating mechanism based on the exponential moving average (EMA) and k-means clustering is introduced to enhance the quality of pseudo-labels. The experimental results on three public datasets demonstrate that our approach has achieved state-of-the-art performance across multiple metrics. This method can significantly reduce annotation costs and reliance on clinical experts, facilitating large-scale dataset training and promoting the adoption of automated analysis in clinical applications.
由于细胞核分割需要大量人工标注，基于细胞核坐标监督的点监督分割近年来受到认可。尽管取得了很大进展，但两个挑战阻碍了弱监督细胞核分割方法的性能：（1）相邻细胞核的稳定有效分割仍然是一个未解决的挑战。（2）现有方法仅依赖于从点标注生成的初始伪标签进行训练，不准确的标签可能导致模型吸收大量噪声信息，从而降低性能。为了解决这些问题，我们提出了一种基于中心点预测和伪标签更新的精确细胞核分割方法。首先，我们设计了一种高斯核机制，该机制采用多尺度高斯掩码进行多分支中心点预测。分割模块利用生成的中心点来促进相邻细胞核的有效分离。接下来，我们引入了一种点引导注意力机制，该机制将分割模块的注意力集中在真实点标签周围，减少伪标签造成的噪声影响。最后，引入了一种基于指数移动平均（EMA）和k均值聚类的标签更新机制，以提高伪标签的质量。在三个公共数据集上的实验结果表明，我们的方法在多个指标上取得了领先的性能。该方法可以显著降低标注成本并减少对临床专家的依赖，便于大规模数据集训练，并促进临床应用中自动化分析的采用。
