IEEE Trans Med Imaging. 2017 Jan;36(1):288-300. doi: 10.1109/TMI.2016.2606380. Epub 2016 Sep 7.
Accurate segmentation of cervical cells in Pap smear images is an important step in automatic pre-cancer identification in the uterine cervix. One of the major segmentation challenges is overlapping of cytoplasm, which has not been well-addressed in previous studies. To tackle the overlapping issue, this paper proposes a learning-based method with robust shape priors to segment individual cell in Pap smear images to support automatic monitoring of changes in cells, which is a vital prerequisite of early detection of cervical cancer. We define this splitting problem as a discrete labeling task for multiple cells with a suitable cost function. The labeling results are then fed into our dynamic multi-template deformation model for further boundary refinement. Multi-scale deep convolutional networks are adopted to learn the diverse cell appearance features. We also incorporated high-level shape information to guide segmentation where cell boundary might be weak or lost due to cell overlapping. An evaluation carried out using two different datasets demonstrates the superiority of our proposed method over the state-of-the-art methods in terms of segmentation accuracy.
准确分割巴氏涂片图像中的宫颈细胞是自动识别宫颈癌前病变的重要步骤。其中一个主要的分割挑战是细胞质的重叠，这在以前的研究中尚未得到很好的解决。为了解决重叠问题，本文提出了一种基于学习的方法，该方法具有强大的形状先验，可用于分割巴氏涂片图像中的单个细胞，以支持对细胞变化的自动监测，这是早期发现宫颈癌的重要前提。我们将这个分割问题定义为具有合适代价函数的多个细胞的离散标记任务。然后，将标记结果输入到我们的动态多模板变形模型中，以进一步细化边界。采用多尺度深度卷积网络来学习不同的细胞外观特征。我们还结合了高层形状信息来指导分割，因为细胞边界可能由于细胞重叠而变得薄弱或丢失。使用两个不同的数据集进行的评估表明，与最先进的方法相比，我们提出的方法在分割准确性方面具有优势。
