作者:何勇军,张雪媛,邵慧丽,丁博
Authors:HE Yong jun,ZHANG Xue yuan,SHAO Hui li,DING Bo摘要:摘要:随着人工智能技术的发展，自动阅片系统在辅助病理医生阅片诊断，提高病理诊断准确率、降低劳动强度等方面起着越来越重要的作用。细胞核的准确分割是影响该自动阅片系统性能的首要因素。由于细胞核、细胞浆与背景之间的边界不清，且细胞之间颜色差异大，对细胞核分割提出了挑战。为解决这一问题，提出了一种基于优化最大极值稳定区域（maximally stable extremal regions，MSER）算法的宫颈细胞核分割方法。该方法首先将图像转换到HSV（hue，saturation，value）颜色空间。然后针对S和V通道做加权组合后，采用优化后的MSER算法处理，获得灰度值均匀的粗分割区域。再利用参数自适应的阈值分割方法进行精细分割。最后通过提取细胞核特征训练人工神经网络分类器来判断分割后得到的结果是否为细胞核。实验表明，该方法能够准确地分割宫颈细胞核。
Abstract:Abstract:With the development of artificial intelligence technology, the automatic reading system plays an increasingly important role in assisting the diagnosis of pathologists, improving the accuracy of pathology diagnosis and reducing labor intensity.Accurate segmentation of the nucleus is the primary factor affecting the performance of the automated reading system. Because the boundary between the nucleus, the cytoplasm and the background is unclear, and the color difference between the cells is large, the nuclear segmentation is challenged. In order to solve this problem, a method of cervical nucleus segmentation based on optimal maximum stability regions(Maximally Stable Extremal Regions, MSER) algorithm is proposed. This method first converts the image to the HSV (Hue, Saturation, Value) color space. Then, after weighted combination of S and V channels, the optimized MSER algorithm is used to obtain a coarse segmentation region with uniform gray values. The parameter segmentation method is used to perform fine segmentation. Finally, the feature extraction technique is used to extract various features from the nuclear image, and the artificial neural network classifier is trained to judge whether the result obtained after segmentation is the nucleus. Experiments show that the method can accurately segment the cervical nucleus

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