Suppr超能文献

使用空间自适应主动物理模型进行重叠细胞核分割。

Overlapping cell nuclei segmentation using a spatially adaptive active physical model.

机构信息

Department of Computer Science, University of Ioannina, Ioannina 45110, Greece.

出版信息

IEEE Trans Image Process. 2012 Nov;21(11):4568-80. doi: 10.1109/TIP.2012.2206041. Epub 2012 Jun 26.

Abstract

A method for the segmentation of overlapping nuclei is presented, which combines local characteristics of the nuclei boundary and a priori knowledge about the expected shape of the nuclei. A deformable model whose behavior is driven by physical principles is trained on images containing a single nuclei, and attributes of the shapes of the nuclei are expressed in terms of modal analysis. Based on the estimated modal distribution and driven by the image characteristics, we develop a framework to detect and describe the unknown nuclei boundaries in images containing two overlapping nuclei. The problem of the estimation of an accurate nucleus boundary in the overlapping areas is successfully addressed with the use of appropriate weight parameters that control the contribution of the image force in the total energy of the deformable model. The proposed method was evaluated using 152 images of conventional Pap smears, each containing two overlapping nuclei. Comparisons with other segmentation methods indicate that our method produces more accurate nuclei boundaries which are closer to the ground truth.

摘要

提出了一种用于分割重叠细胞核的方法,该方法结合了细胞核边界的局部特征和关于细胞核预期形状的先验知识。在包含单个细胞核的图像上训练可变形模型,其行为由物理原理驱动,并且细胞核的形状属性用模态分析表示。基于估计的模态分布并受图像特征驱动,我们开发了一个框架,用于检测和描述包含两个重叠细胞核的图像中的未知细胞核边界。通过使用适当的权重参数成功解决了在重叠区域中准确估计细胞核边界的问题,这些权重参数控制了图像力在可变形模型总能量中的贡献。使用包含两个重叠细胞核的 152 张传统巴氏涂片图像对所提出的方法进行了评估。与其他分割方法的比较表明,我们的方法产生的细胞核边界更准确,更接近真实情况。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验