# 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
This proves that our enhanced processing effectively improves the quality, quantity, and the diversity of the original chromosome dataset, which can promote the model to learn the target features more efficiently. Due to the lack of publicly available large-scale datasets in the field of chromosome segmentation, the chromosome segmentation dataset that we constructed lays a foundation for future research on chromosome segmentation or relevant processing. \begin{table}
\begin{tabular}{c c} \hline \hline
**Models** & **Total Params** \\ \hline Mask-RCNN & 65.121 M \\ PSP-Net & 46.718 M \\ DeepLabV3 & 54.715 M \\ ChroSegNet & 49.608 M \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of parameters of different models and ChroSegNet. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **MIoU (\%)** & **MPA (\%)** & **F1 (\%)** \\ \hline Mask-RCNN & 79.91 & 83.49 & 85.27 \\ PSP-Net & 69.84 & 75.02 & 74.77 \\ DeepLabV3 & 64.95 & 69.55 & 71.34 \\ ChroSegNet & **86.97** & **93.31** & **92.99** \\ \hline \hline \end{tabular} Bold represents the best performance value for each model.  
\end{table} 
Table 3: Segmentation performance analysis of different segmentation models with ChroSegNet. Table 6 shows the performance comparison of U-Net, U-Net+ATT (U-Net with hybrid attention module), U-Net* (U-Net with improved backbone network), and ChroSegNet on our enhanced dataset mentioned above (all three indicators are the average of the corresponding metrics for all categories). It can be observed that the MIoU, MPA, and F1 of the two improved baseline models (U-Net+ATT and U-Net*) are significantly improved, especially U-Net with hybrid attention module. ChroSegNet achieved the best results by combining the two improvements, which confirms the effectiveness of the combination of the two improvements. Figure 7 shows the segmentation mask comparison between ChroSegNet and U-Net on multiple chromosomal microscope images. It can be observed from the comparison of Group A that U-Net still segmented part of the background as chromosome instances by mistake. In contrast, no background was misclassified as chromosome instances by ChroSegNet. In the Group B comparison, a large number of single chromosomes are divided into multiple chromosomes by mistakes in the segmentation mask of U-Net, and some adjacent chromosomes are not effectively segmented. On the contrary, these errors rarely occur in ChroSegNet's mask because ChroSegNet can obtain more critical chromosomal characteristics and location information. In Group C, due to the denser chromosomes, U-Net has yielded more incorrect segmentation masks, including wrongly classifying the entire chromosome or part of the chromosome, and unsegmenting overlapping chromosomes. On the contrary, the segmentation mask of ChroSegNet is more accurate, which benefits from its deep structure and attention information acquisition. In summary, compared with the original U-Net model, ChroSegNet has higher chromosome segmentation accuracy and better robustness. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **MIoU (\%)** & **MPA (\%)** & **F1 (\%)** \\ \hline U-Net & 78.14 & 82.14 & 80.09 \\ U-Net+ATT & 85.50 & 92.60 & 92.13 \\ U-Net* & 80.73 & 85.56 & 83.07 \\ ChroSegNet & **86.97** & **93.31** & **92.99** \\ \hline \hline \end{tabular} Bold represents the best performance value for each model.  
\end{table} 
Table 6: Segmentation performance of different modules based on the baseline model (U-Net). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **MIoU (\%)** & **MPA (\%)** & **F1 (\%)** \\ \hline Original & 66.79 & 79.25 & 79.78 \\ Enhanced & **78.14** & **87.14** & **87.58** \\ \hline \hline \end{tabular} Bold represents the best performance value for each Dateset.  
\end{table} 
Table 5: Enhanced processing effect comparison.