  Karyotyping is of importance for detecting chromosomal aberrations in human
disease. However, chromosomes easily appear curved in microscopic images, which
prevents cytogeneticists from analyzing chromosome types. To address this
issue, we propose a framework for chromosome straightening, which comprises a
preliminary processing algorithm and a generative model called masked
conditional variational autoencoders (MC-VAE). The processing method utilizes
patch rearrangement to address the difficulty in erasing low degrees of
curvature, providing reasonable preliminary results for the MC-VAE. The MC-VAE
further straightens the results by leveraging chromosome patches conditioned on
their curvatures to learn the mapping between banding patterns and conditions.
During model training, we apply a masking strategy with a high masking ratio to
train the MC-VAE with eliminated redundancy. This yields a non-trivial
reconstruction task, allowing the model to effectively preserve chromosome
banding patterns and structure details in the reconstructed results. Extensive
experiments on three public datasets with two stain styles show that our
framework surpasses the performance of state-of-the-art methods in retaining
banding patterns and structure details. Compared to using real-world bent
chromosomes, the use of high-quality straightened chromosomes generated by our
proposed method can improve the performance of various deep learning models for
chromosome classification by a large margin. Such a straightening approach has
the potential to be combined with other karyotyping systems to assist
cytogeneticists in chromosome analysis.
