# ChrSNet: Chromosome Straightening using Self-attention Guided Networks

arXiv: https://arxiv.org/abs/2207.00147 · HTML: https://mlsanity.com/paper/2207.00147/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 1 Jul 2022.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [MedGAN: Medical Image Translation using GANs](https://mlsanity.com/paper/1806.06397/) (2018)
- [Unsupervised Medical Image Translation Using Cycle-MedGAN](https://mlsanity.com/paper/1903.03374/) (2019)
- [Practical Wide-Angle Portraits Correction with Deep Structured Models](https://mlsanity.com/paper/2104.12464/) (2021)
- [Image to Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography](https://mlsanity.com/paper/1906.10089/) (2019)
- [Semi-Supervised Wide-Angle Portraits Correction by Multi-Scale Transformer](https://mlsanity.com/paper/2109.08024/) (2021)
- [Varifocal-Net: A Chromosome Classification Approach using Deep Convolutional Networks](https://mlsanity.com/paper/1810.05943/) (2018)
- [A Multi-attribute Controllable Generative Model for Histopathology Image Synthesis](https://mlsanity.com/paper/2111.06398/) (2021)
- [A Novel Application of Image-to-Image Translation: Chromosome Straightening Framework by Learning from a Single Image](https://mlsanity.com/paper/2103.02835/) (2021)

## Built on by (most cited first, 3 of 3)

- [Benchmarking PathCLIP for Pathology Image Analysis](https://mlsanity.com/paper/2401.02651/) (2024)
- [Masked conditional variational autoencoders for chromosome straightening](https://mlsanity.com/paper/2306.14129/) (2023)
- [Perturb-and-Restore: Simulation-driven Structural Augmentation Framework for Imbalance Chromosomal Anomaly Detection](https://mlsanity.com/paper/2604.00854/) (2026)

## Abstract (arXiv)

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.