Chromosome Straightening

Chromosome straightening is a crucial preprocessing step in cytogenetics, aiming to correct the naturally curved shapes of chromosomes in microscopic images to facilitate accurate analysis. Recent research focuses on deep learning approaches, employing architectures like variational autoencoders, self-attention guided networks, and Vision Transformer-based GANs, to learn the complex mapping between curved and straightened chromosome representations while preserving crucial banding patterns and structural details. These advancements improve the accuracy of downstream tasks such as chromosome classification and aberration detection, ultimately enhancing the efficiency and reliability of karyotyping for disease diagnosis and radiation exposure assessment.