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

Song, Sifan ORCID: 0000-0002-7940-650X, Huang, Daiyun, Hu, Yalun, Yang, Chunxiao, Meng, Jia ORCID: 0000-0003-3455-205X, Ma, Fei ORCID: 0000-0001-6099-480X, Coenen, Frans ORCID: 0000-0003-1026-6649, Zhang, Jiaming and Su, Jionglong (2021) A Novel Application of Image-to-Image Translation: Chromosome Straightening Framework by Learning from a Single Image. In: 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2021-10-23 - 2021-10-25.

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Abstract

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.

Item Type:	Conference or Workshop Item (Unspecified)
Uncontrolled Keywords:	Conditional Generative Adversarial Networks, Curved Chromosomes, Image-to-Image Translation, Straightening Framework
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	11 Oct 2021 08:38
Last Modified:	14 Mar 2024 21:44
DOI:	10.1109/CISP-BMEI53629.2021.9624383
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3140038