  Karyotyping is an important procedure to assess the possible existence of
chromosomal abnormalities. However, because of the non-rigid nature,
chromosomes are usually heavily curved in microscopic images and such deformed
shapes hinder the chromosome analysis for cytogeneticists. In this paper, we
present a self-attention guided framework to erase the curvature of
chromosomes. The proposed framework extracts spatial information and local
textures to preserve banding patterns in a regression module. With
complementary information from the bent chromosome, a refinement module is
designed to further improve fine details. In addition, we propose two dedicated
geometric constraints to maintain the length and restore the distortion of
chromosomes. To train our framework, we create a synthetic dataset where curved
chromosomes are generated from the real-world straight chromosomes by
grid-deformation. Quantitative and qualitative experiments are conducted on
synthetic and real-world data. Experimental results show that our proposed
method can effectively straighten bent chromosomes while keeping banding
details and length.
