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.

中文翻译：

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图像到图像翻译的一种新应用：通过从单个图像中学习来实现染色体校正框架

在医学成像中，染色体拉直在染色体的病理研究和细胞遗传图谱的开发中起着重要作用。尽管存在用于矫直任务的不同方法，但它们大多是几何算法，其输出以锯齿状边缘或具有不连续带状图案的碎片为特征。为了解决几何算法中的缺陷，我们提出了一种基于图像到图像转换的新颖框架，以学习相关的映射依赖性，以合成具有不间断条带模式和保留细节的直化染色体。此外，为了避免输入染色体不足的陷阱，我们仅使用一个弯曲的染色体图像作为训练模型来构建增强的数据集。基于此框架，我们应用了两种流行的图像到图像翻译体系结构，U形网络和条件生成对抗网络，以评估其效果。在包含642个现实世界染色体的数据集上进行的实验证明，与几何方法相比，通过渲染逼真的和连续的染色体详细信息，我们的框架在矫直性能方面具有优越性。此外，我们经过整理的结果改善了染色体分类，平均准确度达到了0.98％-1.39％。