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, they are mostly geometric algorithms 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 comprising 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, achieving 0.98%-1.39% in mean accuracy.

翻译：在医学成像中,染色体的整形在染色体病理学研究以及细胞基因图的开发方面起着重要作用。虽然在整形任务中存在不同的方法,但它们大多是几何算法,其产出的特征是带宽的边缘或碎片以及断断断的带宽模式。为解决几何算法的缺陷,我们提议了一个基于图像到图像翻译的新框架,以学习对以不间断的带宽模式和保存的细节合成直线染色体的相关绘图依赖性。此外,为避免输入的染色体不足的陷阱,我们只用单一的曲线染色体图像来构建一个强化的数据集。基于这个框架,我们应用两种流行的图像到图像转换结构、U形状网络和有条件的基因化对抗网络来评估其效力。在由642个真实世界染色体组成的数据集上进行的实验显示了我们框架的优越性,与通过制定现实的和连续的染色体精确度的精确度来调整业绩的几何方法相比,改进了我们框架的精确度。此外,我们用精确度来改进了0.19 %的精确度,并改进了我们的精确度。