  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.
