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arXiv 2021年

作者： Song, Sifan Huang, Daiyun Hu, Yalun Yang, Chunxiao Meng, Jia Ma, Fei Coenen, Frans Zhang, Jiaming Su, Jionglong Department of Mathematical Sciences Xi'An Jiaotong-Liverpool University Suzhou215123 China Department of Biological Sciences Xi'An Jiaotong-Liverpool University Suzhou215123 China Department of Applying Algorithm Engineering Synsense Ltd. Chengdu610041 China Suzhou Sano Precision Medicine Ltd. Suzhou215123 China Department of Applied Mathematics Xi'An Jiaotong-Liverpool University Suzhou215123 China Department of Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Xi'An Jiaotong-Liverpool University Suzhou215123 China

In medical imaging, chromosome straightening plays a significant role in the pathological study of chromosomes and in the development of cytogenetic maps. Whereas different approaches exist for the straightening task, typically geometric algorithms are used whose outputs are characterized by jagged edges or fragments with discontinued banding patterns. To address the flaws in the geometric algorithms, we propose a novel framework based on image-to-image translation to learn a pertinent mapping dependence for synthesizing straightened chromosomes with uninterrupted banding patterns and preserved details. In addition, to avoid the pitfall of deficient input chromosomes, we construct an augmented dataset using only one single curved chromosome image for training models. Based on this framework, we apply two popular image-to-image translation architectures, U-shape networks and conditional generative adversarial networks, to assess its efficacy. Experiments on a dataset comprised of 642 real-world chromosomes demonstrate the superiority of our framework, as compared to the geometric method in straightening performance, by rendering realistic and continued chromosome details. Furthermore, our straightened results improve the chromosome classification by 0.98%-1.39% mean accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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