  Chromosome karyotype analysis is of great clinical importance in the
diagnosis and treatment of diseases, especially for genetic diseases. Since
manual analysis is highly time and effort consuming, computer-assisted
automatic chromosome karyotype analysis based on images is routinely used to
improve the efficiency and accuracy of the analysis. Due to the strip shape of
the chromosomes, they easily get overlapped with each other when imaged,
significantly affecting the accuracy of the analysis afterward. Conventional
overlapping chromosome segmentation methods are usually based on manually
tagged features, hence, the performance of which is easily affected by the
quality, such as resolution and brightness, of the images. To address the
problem, in this paper, we present an adversarial multiscale feature learning
framework to improve the accuracy and adaptability of overlapping chromosome
segmentation. Specifically, we first adopt the nested U-shape network with
dense skip connections as the generator to explore the optimal representation
of the chromosome images by exploiting multiscale features. Then we use the
conditional generative adversarial network (cGAN) to generate images similar to
the original ones, the training stability of which is enhanced by applying the
least-square GAN objective. Finally, we employ Lovasz-Softmax to help the model
converge in a continuous optimization setting. Comparing with the established
algorithms, the performance of our framework is proven superior by using public
datasets in eight evaluation criteria, showing its great potential in
overlapping chromosome segmentation

Computer Vision and Pattern Recognition;Artificial Intelligence