Deep learning-based chromosome segmentation and extraction: A comprehensive review of methodologies, challenges, and future directions

Abstract

Chromosome karyotyping is fundamental to cytogenetics, facilitating the diagnosis of genetic disorders and malignancies through detailed structural analysis of chromosomes. A major technical challenge is the precise segmentation and extraction of complete, non-overlapping chromosomes, especially in cases involving dense chromosome clusters or significant morphological variation. Although deep learning has achieved notable success in general image processing, its application to chromosomal analysis has only recently gained momentum, and comprehensive evaluations remain scarce. This review systematically examines recent advances in deep learning-based chromosome segmentation and extraction, summarizing prevailing methodologies and key limitations. It traces the evolution from early convolutional neural networks to encoder-decoder architectures and generative models, highlighting advances in spatial detail recovery, robustness against overlapping structures, and domain adaptation. Furthermore, the paper categorizes chromosomal segmentation into semantic, instance, and hybrid paradigms, elucidates methodological trends such as the incorporation of biological priors and the adoption of multi-task learning, and discusses practical and cognitive challenges that hinder clinical implementation. By providing a comprehensive overview and outlining future directions—including explainable AI and synthetic data augmentation—this work aims to accelerate the development of intelligent, fully automated chromosome karyotyping systems.