Learning Disentangled Representation for Chromosome Straightening

Abstract

Chromosome straightening plays an important role in karyotype analysis. Common straightening methods usually adopt geometric algorithms, which tend to affect the chromosome banding patterns in the process of straightening, resulting in feature changes, loss of details, and poor generalization. To solve these problems, this paper proposes a novel straightening method based on disentanglement representation learning. Our method consists of two main components: the Disentanglement Representation Encoder (DRE) and the Straightening Generator (SG), where DRE discovers and disentangles the bent representation and the content representation in the latent space, while SG is used to generate the straightened chromosome images based on the disentangled representations. Leveraging the bent representation and the content representation disentangled by DRE, our method produces the straightened representation by reducing the bent representation while keeping the content representation unchanged, making straightening chromosome without changing its banding patterns possible. Evaluation results on both the Frechet Initiation Distance (FID) and the Downstream Classification Accuracy (DCA) metrics show that our method achieves good performance.