# ChroSegNet: An Attention-Based Model for Chromosome Segmentation with Enhanced Processing
###### Abstract  
In modern medical diagnosis, the karyotype analysis for human chromosome is clinically significant for the diagnosis and treatment of genetic diseases. In such an analysis, it is critically important to segment the banded chromosomes. Chromosome segmentation, however, is technically challenging due to the variable chromosome features, the complex background noise, and the uneven image quality of the chromosome images. Owing to these technical challenges, the existing deep-learning-based algorithms would have severe overfitting problems and are ineffective in the segmentation task. In this paper, we propose a novel chromosome segmentation model with our enhanced chromosome processing, namely ChroSegNet. First, we develop enhanced chromosome processing techniques to realize the quality and quantity enhancement of the chromosome data, leading to the chromosome segmentation dataset for our subsequent network training. Second, we propose our novel chromosome segmentation model \"ChroSegNet\" based on U-Net. According to the characteristics of chromosome data, we have not only improved the baseline structure but also incorporate the hybrid attention module to ChroSegNet, which can extract the key feature information and location information of chromosome. Finally, we evaluated ChroSegNet on our chromosome segmentation dataset and obtained the MPA of 93.31% and the F1-score of 92.99%. Experimental results show that ChroSegNet not only outperforms the representative segmentation models in chromosome segmentation performance but also has a lightweight model structure. We believe that our proposed ChroSegNet is highly promising in future applications of genetic measurement and diagnosis. deep learning; chromosome segmentation; U-Net; attention mechanism; enhanced processing Article  
A Note: **ChroSegNet: An Attention-Based Model for Chromosome Segmentation with Enhanced Processing**  
**[PERSON] 1 School of Computing, Beijing Technology and Business University, Beijing 100048, China 1  
sorted to form a karyotype image with 24 types of chromosomes [3]. For example, Figure 1a shows an image of the 46 types of chromosomes under a 100\(\times\) microscope and Figure 1b shows the karyotype image of these chromosomes in pairs. In early manual karyotyping, doctors would be required to manually extract and classify the chromosomes for analysis, which was not only time-consuming and labor-intensive but also prone to human errors due to its tedious process [4]. Fortunately, the emergence of automatic karyotype analysis system has greatly reduced the workload of karyotype analysis for doctors. It can automatically complete the operations such as chromosome segmentation and classification and finally generate a karyotype map for medical diagnosis. At present, most existing karyotyping systems employ traditional karyotyping methods, which mainly rely on the manual features to complete the chromosome segmentation and classification. For instance, the mainstream traditional chromosome segmentation methods include threshold-based segmentation methods [5], watershed-based segmentation methods [6], fuzzy-clustering-based segmentation methods [7], geometric feature-based segmentation methods [8], etc. The above methods are highly dependent on the manual features and preset parameters thus being limited in their scope of applications. For example, the threshold-based segmentation algorithms rely heavily on the pixel's grayscale information and do not consider the spatial information between pixels. However, due to the strong variability of the chromosome morphological structure and complex distribution, traditional rule-based segmentation methods cannot adapt to such complex situations. In addition, traditional methods still require a lot of manual intervention, which is time-consuming and labor-intensive. Therefore, traditional image segmentation methods still cannot solve the problem of chromosome segmentation well.