Abstract
The chromosome karyotyping task is vital and indispensable but tedious work for birth defect diagnosis and biomedical research. In this work, we tackle chromosome automatic karyotyping using a multi-stages chromosome segmentation and mixed classification method. Firstly, we apply a global binary threshold-based method to segment the metaphase chromosome microscope grayscale image into several image slices, consisting of chromosome instances and chromosome clusters. Afterward, we propose a mixed chromosome classification method for identifying a given image is a chromosome cluster or corresponding instance label. After that, we use a deep learning-based approach to segment chromosome cluster images into chromosome instances and apply the mixed chromosome classification model to recognize their corresponding labels. Finally, we synthesize a chromosome karyotype from all corresponding instances and labels. In the mixed classification stage, the proposed method yields 99.53 ± 0.23% classification accuracy on the clinical dataset. In segmentation stages, the proposed method achieves 90.81% comprehensive segmentation accuracy and 85.00% instance segmentation accuracy with 90.63% \(AP_{50}\) precision. The experimental results show that our proposed method is promising for solving chromosome segmentation and classification task of the clinical chromosome automatic karyotyping.
This work was supported by Key-Area Research and Development Program of Guangdong Province(No.2019B010137003), NationalKey-Area Research and Development Program of China (2018YFB1404402), Guangdong Science and Technology Fund (No.2016B030305006, No.2018A07071702, No.201804010314), Guangzhou Science & Technology Fund (No.201804010314), VeChain Foundation (No.SCNU-2018-01).
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Lin, C., Zhao, G., Yin, A., Ding, B., Guo, L., Chen, H. (2020). A Multi-Stages Chromosome Segmentation and Mixed Classification Method for Chromosome Automatic Karyotyping. In: Wang, G., Lin, X., Hendler, J., Song, W., Xu, Z., Liu, G. (eds) Web Information Systems and Applications. WISA 2020. Lecture Notes in Computer Science(), vol 12432. Springer, Cham. https://doi.org/10.1007/978-3-030-60029-7_34
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DOI: https://doi.org/10.1007/978-3-030-60029-7_34
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