Guo Yanen, Xu Xiaoyin, Wang Yuanyuan, Wang Yaming, Xia Shunren, Yang Zhong
Key laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China.
Microsc Res Tech. 2014 Aug;77(8):547-59. doi: 10.1002/jemt.22373. Epub 2014 Apr 29.
Muscle fiber images play an important role in the medical diagnosis and treatment of many muscular diseases. The number of nuclei in skeletal muscle fiber images is a key bio-marker of the diagnosis of muscular dystrophy. In nuclei segmentation one primary challenge is to correctly separate the clustered nuclei. In this article, we developed an image processing pipeline to automatically detect, segment, and analyze nuclei in microscopic image of muscle fibers. The pipeline consists of image pre-processing, identification of isolated nuclei, identification and segmentation of clustered nuclei, and quantitative analysis. Nuclei are initially extracted from background by using local Otsu's threshold. Based on analysis of morphological features of the isolated nuclei, including their areas, compactness, and major axis lengths, a Bayesian network is trained and applied to identify isolated nuclei from clustered nuclei and artifacts in all the images. Then a two-step refined watershed algorithm is applied to segment clustered nuclei. After segmentation, the nuclei can be quantified for statistical analysis. Comparing the segmented results with those of manual analysis and an existing technique, we find that our proposed image processing pipeline achieves good performance with high accuracy and precision. The presented image processing pipeline can therefore help biologists increase their throughput and objectivity in analyzing large numbers of nuclei in muscle fiber images.
肌纤维图像在许多肌肉疾病的医学诊断和治疗中起着重要作用。骨骼肌纤维图像中的细胞核数量是诊断肌肉萎缩症的关键生物标志物。在细胞核分割中，一个主要挑战是正确分离聚集的细胞核。在本文中，我们开发了一种图像处理流程，用于自动检测、分割和分析肌纤维微观图像中的细胞核。该流程包括图像预处理、孤立细胞核识别、聚集细胞核识别与分割以及定量分析。首先使用局部大津阈值从背景中提取细胞核。基于对孤立细胞核形态特征（包括其面积、紧凑度和主轴长度）的分析，训练并应用贝叶斯网络从所有图像中的聚集细胞核和伪像中识别孤立细胞核。然后应用两步改进的分水岭算法分割聚集细胞核。分割后，可对细胞核进行量化以进行统计分析。将分割结果与手动分析和现有技术的结果进行比较，我们发现我们提出的图像处理流程具有高精度和高精准度，性能良好。因此，所提出的图像处理流程可以帮助生物学家在分析肌纤维图像中的大量细胞核时提高通量和客观性。
