Chengyi College, Jimei University, Xiamen, 361021, Fujian, China.
Shanghai Institute of Technology, Shanghai, 200235, China.
Sci Rep. 2024 Jun 20;14(1):14210. doi: 10.1038/s41598-024-64583-7.
Regular screening for cervical cancer is one of the best tools to reduce cancer incidence. Automated cell segmentation in screening is an essential task because it can present better understanding of the characteristics of cervical cells. The main challenge of cell cytoplasm segmentation is that many boundaries in cell clumps are extremely difficult to be identified. This paper proposes a new convolutional neural network based on Mask RCNN and PointRend module, to segment overlapping cervical cells. The PointRend head concatenates fine grained features and coarse features extracted from different feature maps to fine-tune the candidate boundary pixels of cell cytoplasm, which are crucial for precise cell segmentation. The proposed model achieves a 0.97 DSC (Dice Similarity Coefficient), 0.96 TPRp (Pixelwise True Positive Rate), 0.007 FPRp (Pixelwise False Positive Rate) and 0.006 FNRo (Object False Negative Rate) on dataset from ISBI2014. Specially, the proposed method outperforms state-of-the-art result by about on DSC, on TPRp and on FNRo respectively. The performance metrics of our model on dataset from ISBI2015 are slight better than the average value of other approaches. Those results indicate that the proposed method could be effective in cytological analysis and then help experts correctly discover cervical cell lesions.
定期进行宫颈癌筛查是降低癌症发病率的最佳手段之一。在筛查中自动进行细胞分割是一项必不可少的任务，因为它可以更好地理解宫颈细胞的特征。细胞细胞质分割的主要挑战是，细胞簇中的许多边界极难识别。本文提出了一种新的基于 Mask RCNN 和 PointRend 模块的卷积神经网络，用于分割重叠的宫颈细胞。PointRend 头将细粒度特征和从不同特征图中提取的粗特征连接起来，以微调细胞质候选边界像素，这对于精确的细胞分割至关重要。该模型在 ISBI2014 数据集上的 DSC（Dice 相似系数）为 0.97，TPRp（逐像素真阳性率）为 0.96，FPRp（逐像素假阳性率）为 0.007，FNRo（目标假阴性率）为 0.006。特别地，该方法在 DSC 上的表现比最先进的方法高出约 ，在 TPRp 上高出约 ，在 FNRo 上高出约 。该模型在 ISBI2015 数据集上的性能指标略优于其他方法的平均值。这些结果表明，该方法在细胞学分析中可能是有效的，从而帮助专家正确发现宫颈细胞病变。
