A New Convolutional Neural Network Architecture for Automatic Segmentation of Overlapping Human Chromosomes

Sifan Song, Tianming Bai, Yanxin Zhao, Wenbo Zhang, Chunxiao Yang, Jia Meng, Fei Ma*, Jionglong Su*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


In clinical diagnosis, karyotyping is carried out to detect genetic disorders due to chromosomal aberrations. Accurate segmentation is crucial in this process that is mostly operated by experts. However, it is time-consuming and labor-intense to segment chromosomes and their overlapping regions. In this research, we look into the automatic segmentation of overlapping pairs of chromosomes. Different from standard semantic segmentation applications that mostly detect object regions or boundaries, this study attempts to predict not only non-overlapping regions but also the order of superposition and opaque regions of the underlying chromosomes. We propose a novel convolutional neural network called Compact Seg-UNet with enhanced deep feature learning capability and training efficacy. To address the issue of unrealistic images in use characterized by overlapping regions of higher color intensities, we propose a novel method to generate more realistic images with opaque overlapping regions. On the segmentation performance of overlapping chromosomes for this new dataset, our Compact Seg-UNet model achieves an average IOU score of 93.44% ± 0.26 which is significantly higher than the result of a simplified U-Net reported by literature by around 6.08%. The corresponding F1 score also increases from 0.9262 ± 0.1188 to 0.9596 ± 0.0814.

Original languageEnglish
Pages (from-to)285-301
Number of pages17
JournalNeural Processing Letters
Issue number1
Publication statusPublished - Feb 2022


  • Automatic segmentation
  • Compact Seg-UNet
  • Convolutional neural networks
  • Deep learning
  • Overlapping chromosomes

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