# ChroSegNet: An Attention-Based Model for Chromosome Segmentation with Enhanced Processing
## 4 Experiments and Results
### Evaluation Metrics and Chromosome Segmentation Results
#### 4.3.2 Chromosome Segmentation Results  
Since there are no publicly available chromosome datasets, we trained our proposed model on the chromosome segmentation dataset we constructed. As there is no publicly available code on chromosome segmentation, we compare the chromosome segmentation performance of ChroSegNet with representative segmentation models in recent years to demonstrate its effectiveness. Furthermore, we conducted separate ablation experiments to verify the effectiveness of our enhanced processing and model improvements. Table 2 shows the segmentation results by ChroSegNet for 24 categories of chromosome instances based on the above three metrics. Classes 1-22 denote chromosome classes 1-22, and classes x and y correspond to the two sex chromosomes, respectively. It can be observed from the table that ChroSegNet achieves the best IoU and F1-score on class 1, reaching 90.17% and 94.83%, respectively. As for class 2, our model achieves the best PA reaching 95.29%. The segmentation results for smaller chromosomes, e.g., class 22 and class Y, are not that high but the lowest IoU score can still reach more than 80%, and the lowest F1-score can reach more than 88%. The results show that ChroSegNet can achieve high precision and robust segmentation for each category of chromosome. We compare and analyze ChroSegNet with other segmentation models in terms of segmentation performance and model parameters shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class (No. )** & **IoU (\%)** & **PA (\  
Obviously, ChroSegNet achieves the best performance on every evaluation metric while the DeepLabV3's performance is the lowest. We think that the poor performance of DeepLabV3 is due to its hollow convolution structure and noncontinuous convolution kernel. In fact, the microscopy images of chromosome contain a large number of small chromosomes, and the DeepLabV3's discontinuous convolutional structure can easily lead to the loss of small target features. Although the segmentation performance of Mask-RCNN is better than that of DeepLabV3 and PSP-Net, it still significantly underperforms compared to ChroSegNet. We believe this is caused by the large size of Mask-RCNN, which cannot be fully trained on small-scale datasets due to its complex structure and huge number of parameters. In summary, although the above benchmarking networks can have good segmentation performance in their respective fields, they still cannot achieve high-precision instance segmentation for chromosomes. In contrast, we can see that ChroSegNet shows not only a significantly higher segmentation accuracy as 93.31% but also an F1 value as high as 92.99%, which indicates that it can well balance segmentation accuracy and recall rate. This indicates that ChroSegNet is more suitable for chromosome segmentation than other models. Table 4 shows the number of parameters for each segmentation model, which reflect the computing resources that a model needs to occupy. It can be seen from Table 4 that Mask-RCNN requires the most computing resources. PSP-Net has the fewest parameters due to its simple structure. Although ChroSegNet adopts hybrid attention modules and further deepens the network structure, the number of parameters of ChroSegNet remains at a low level, i.e., only 2.89 M more parameters than PSP-Net. This data shows that our model is more lightweight and can be considered for practical application in the future. Table 5 shows the effect of our enhanced processing by comparing segmentation results of U-Net on the original dataset and our chromosome segmentation dataset. It can be observed from the table that the final result of U-Net training on the chromosome segmentation dataset has a huge improvement compared with the results obtained on the original dataset. Specifically, the MIoU has increased by 11.35%, the MPA has increased by 7.89%, and the F1-score has increased by 7.80%.