摘要
染色体核型分析是细胞遗传学研究的主要技术之一,在现代医学治疗和诊断中有重要的作用.通常在染色体核型分析的过程中,首先需要在染色体中期图像中分割出单条染色体,然后再对染色体逐一进行分析、比较、排序和分类.由于传统的基于几何及基于统计的分割和分类的辅助工具精度低,辅助作用有限,因此在实际工作中仍然需要医生花费大量的时间和精力进行人工核型分析.为此提出一种基于卷积神经网络和几何优化的染色体核型分析新方法,利用Mask R⁃CNN(Region⁃Convolutional Neural Networks)从染色体中期图像中分割出染色体,并训练一个新型多输入的卷积神经网络对分割后的单条染色体进行分类;还提出一种全新的基于局部特征的染色体分割数据合成方法对分割数据集进行扩充.此外,为了保证分类训练数据的一致性,提出一种基于中线的染色体伸直几何优化算法.实验结果表明提出的方法在自动核型分析中表现优秀.
Karyotype analysis is one of the main techniques of cytogenetics through medical image processing,which plays an important role in modern medical diagnosis and treatment.The process of human karyotype analysis contains two key components.Firstly,chromosomes are segmented from metaphase chromosome digital images taken under a microscope.Then,chromosomes are analyzed,compared,ordered and classified one by one carefully.Under this procedure,the operation on segmentation and classification is cumbersomely time consuming,where traditional geometric or statistical methods only have limited effect due to low accuracy.Thus,in most conditions,human effort is still heavily required to monitor the workflow and correct the errors.In this paper,we present an integrated workflow to segment out and classify chromosomes automatically using a combination of Convolutional Neural Networks(CNN)and geometric optimization.We investigate Mask R⁃CNN(Region⁃CNN)to segment out chromosomes from metaphase chromosome images and train a CNN to classify the sub⁃images.To improve the performance of the segmentation network,we adapt a new local feature⁃based approach to synthesize images on the annotated data.Furthermore,we develop a geometric algorithm to straighten the chromosomes before classification to ensure the consistency on the training data.Experimental results demonstrate that our approach has better performance on automatic karyotype analysis.
作者
李康
谢宁
李旭
谭凯
Li Kang;Xie Ning;Li Xu;Tan Kai(School of Computer Science and Engineering,University of Electronic Science and Technology of China,Chengdu,611731,China;Glasgow College,University of Electronic Science and Technology of China,Chengdu,611731,China)
出处
《南京大学学报（自然科学版）》
CAS
CSCD
北大核心
2020年第1期116-124,共9页
Journal of Nanjing University（Natural Science）
基金
国家自然科学基金(61602088)
中央高校基本科研业务费基础研究项目(Y03019023601008011)
关键词
深度学习
核型分析
医疗图像处理
几何优化
deep learning
karyotype analysis
medical image processing
geometry optimization
