  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.
