An interactive nuclei segmentation framework with Voronoi diagrams and weighted convex difference for cervical cancer pathology images

作者：Lin Yang, Yuanyuan Lei, Zhenxing Huang, Mengxiao Geng, Zhou Liu, Baijie Wang, Dehong Luo, Wenting Huang, Dong Liang, Zhi‐Feng Pang, Zhanli Hu · 发表于：Physics in Medicine and Biology · 年份：2023 · DOI：10.1088/1361-6560/ad0d44 · 被引用次数：3 · 研究领域：AI in cancer detection、Radiomics and Machine Learning in Medical Imaging、Digital Imaging for Blood Diseases

Abstract Objective. Nuclei segmentation is crucial for pathologists to accurately classify and grade cancer. However, this process faces significant challenges, such as the complex background structures in pathological images, the high-density distribution of nuclei, and cell adhesion. Approach. In this paper, we present an interactive nuclei segmentation framework that increases the precision of nuclei segmentation. Our framework incorporates expert monitoring to gather as much prior information as possible and accurately segment complex nucleus images through limited pathologist interaction, where only a small portion of the nucleus locations in each image are labeled. The initial contour is determined by the Voronoi diagram generated from the labeled points, which is then input into an optimized weighted convex difference model to regularize partition boundaries in an image. Specifically, we provide theoretical proof of the mathematical model, stating that the objective function monotonically decreases. Furthermore, we explore a postprocessing stage that incorporates histograms, which are simple and easy to handle and prevent arbitrariness and subjectivity in individual choices. Main results. To evaluate our approach, we conduct experiments on both a cervical cancer dataset and a nasopharyngeal cancer dataset. The experimental results demonstrate that our approach achieves competitive performance compared to other methods. Significance. The Voronoi diagram in the paper ser...