# ChroSegNet: An Attention-Based Model for Chromosome Segmentation with Enhanced Processing
## 5 Conclusions  
In this paper, we design our ChroSegNet model with an effective attention mechanism for accurate chromosome segmentation. On the basis of considering the lightweight model, our ChroSegNet not only has a deep network structure but also has the hybrid attention structure, which is responsible for simultaneously extracting key features and position information of chromosomes. Experimental results show that ChroSegNet is more suitable than most CNN models to deal with chromosomes with complex structure and changeable position. To construct a dataset for our modeling training, we cooperated with the laboratory to acquire chromosome data and proposed enhanced processing to enhance the quality and quantity of chromosome data, resulting in the chromosome segmentation dataset which with large scale and high quality. Our experimental results show that the performance of the segmentation model trained with our dataset is better than that trained with the original dataset.  
However, the current ChroSegNet is still limited in the following aspects. On the one hand, the segmentation performance of ChroSegNet is relatively limited for the same class of chromosomes with large deformation. For this limitation, we plan to design and incorporate additional branches in ChroSegNet to learn and utilize the chromosome shape information for improved segmentation accuracy in our future work. On the other hand, the segmentation performance of ChroSegNet for overlapping chromosomes still needs to be improved. We plan to further optimize the network structure in future work, such as attempting to model ROI as multiple layers and detecting overlapping chromosomes separately during segmentation.  
Writing--original draft, X.C.; writing--review and editing, X.C. and Q.C.; software, X.C.; validation, Q.C., N.M. and H.L.; conceptualization, X.C., Q.C. and N.M.; methodology, Q.C. and N.M.; formal analysis, Q.C. and H.L.; resources, N.M.; supervision, H.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 7: Comparison of segmentation masks between ChroSegNet and U-Net on chromosome microscopy images. From left to right are the original image, the U-Net segmentation mask, and the ChroSegNet segmentation mask. (**a**) Group A comparison; (**b**) Group B comparison; (**c**) Group C comparison.  
Institutional Review Board Statement: Institutional Review Board Statement: Approval document for clinical scientific research projects of the Medical Ethics Committee of the Chinese People's Liberation Army General Hospital. approval number: S2018-132-01.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patient(s) to publish this paper.  
Data Availability Statement: Data available on request due to restrictions eg privacy or ethical The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the data involves patient chromosome data.  
The authors declare no conflict of interest.