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In medical imaging, chromosome straightening plays a significant role in thepathological study of chromosomes and in the development of cytogenetic maps.Whereas different approaches exist for the straightening task, typicallygeometric algorithms are used whose outputs are characterized by jagged edgesor fragments with discontinued banding patterns. To address the flaws in thegeometric algorithms, we propose a novel framework based on image-to-imagetranslation to learn a pertinent mapping dependence for synthesizingstraightened chromosomes with uninterrupted banding patterns and preserveddetails. In addition, to avoid the pitfall of deficient input chromosomes, weconstruct an augmented dataset using only one single curved chromosome imagefor training models. Based on this framework, we apply two popularimage-to-image translation architectures, U-shape networks and conditionalgenerative adversarial networks, to assess its efficacy. Experiments on adataset comprised of 642 real-world chromosomes demonstrate the superiority ofour framework, as compared to the geometric method in straighteningperformance, by rendering realistic and continued chromosome details.Furthermore, our straightened results improve the chromosome classification by0.98%-1.39% mean accuracy.

A Novel Application of Image-to-Image Translation: Chromosome Straightening Framework by Learning from a Single Image