Chromosome Segmentation via Data Simulation and Shape Learning

Karyotyping, consisting of single chromosome segmentation and classification, is widely used in the cytogenetic analysis for chromosome abnormality detection. Many studies have reported automatic chromosome classification with high accuracy. Nevertheless, they usually require manual chromosome segmentation beforehand. There are two critical issues in automatic chromosome segmentation: 1) scarce annotated images for model training, and 2) multiple region combinations to form single chromosomes. In this study, two simulation strategies are proposed for training data argumentation to alleviate data scarcity. Besides, we present an optimization-based shape learning method to evaluate the shape of formed single chromosomes, which achieve the global minimum loss when segmented regions are correctly combined. Experiments on a public dataset demonstrate the effectiveness of the proposed method. The data simulation strategy has significantly increased the segmentation results by 15.8% and 46.3% of the Dice coefficient on non-overlapped and overlapped regions. Moreover, the proposed optimization-based method separates overlapped chromosomes with an accuracy of 96.2%.