# An Enhanced Feature Extraction Network for Medical Image Segmentation
## 4 Experiment
### Datasets and Results
#### 4.2.4 Results on 2018 Data Science Bowl Challenge  
The main task of this challenge is to detect nuclei in images. Cell nucleus identification helps researchers to identify each cell in an image, thus helping researchers understand potential biological processes. By automatically segmenting the nucleus, doctors can quickly diagnose the disease and treat it. Most genetic disease analysis is based on identifying the nucleus, because most of the 30 trillion cells in the human body contain a nucleus filled with DNA, and DNA is the genetic code for programming each cell. The dataset  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Method** & **DSC (\%)** & **mIoU (\%)** & **Recall (\%)** & **Precision (\%)** \\ \hline U-Net [7] & 87.46 & 80.25 & 90.66 & 88.37 \\ ResUNet++ [51] & 87.99 & 81.00 & 88.92 & 90.57 \\ DoubleU-Net [12] & 89.62 & 82.12 & 87.80 & 94.59 \\ LeViT-UNet [49] & 88.32 & 81.72 & 90.83 & 89.66 \\ TransUNet [48] & 84.99 & 77.00 & 89.82 & 84.73 \\ Attention-UNet [59] & 88.34 & 81.49 & 89.01 & 91.53 \\ BAT [60] & 91.2 & 84.3 & - & - \\ EFEN & 89.92 & 82.25 & 88.10 & 94.71 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparisons on ISIC-2018.  
includes 670 nuclei images. The images are of varied resolutions, and we resize all images to 384 \(\times\) 288. We first divide the dataset into a training set, verification set and testing set according to the ratio of 8:1:1. By using 25 kinds of data augmentation methods to expand the dataset, we obtain 13,936 training samples and 1742 verification samples. Table 5 shows the experimental comparisons of various methods on this dataset.  
Table 5 shows the results for the 2018 Data Science Bowl challenge. We observe that EFEN achieves a validation DSC of 91.65%, exceeding baseline U-Net (88.75%) by 2.9%. Compared with recent works, EFEN outperforms DoubleU-Net, with 0.32% and 0.24% improvements on DSC and mean IOU, respectively. SSFormer-L is based on a transformer which needs more data for training, and CNN will be more suitable for the smaller dataset.  
In Figure 6, we visualize the segmentation results of our method compared to baseline U-Net. Our method exhibits an advantage in effectively segmenting small objects. As shown in the first, fourth and sixth columns, we observe that the nuclei missed by U-Net can be identified by our method. In the second and third columns, we observe that our method is better than U-Net in identifying targets. Although our method may not be optimal, as shown by previous experiments, our method is better than the baseline method and some recent work.  
Figure 5: Visualization results of EFEN on ISIC-2018 test set. Each column includes the original input image and its corresponding mask, the result of baseline model U-Net and EFEN. The red box shows the comparisons in the same position.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Method** & **DSC (\%)** & **mIoU (\%)** & **Recall (\%)** & **Precision (\%)** \\ \hline U-Net [7] & 88.75 & 80.80 & 92.08 & 87.22 \\ UNet++ [9] & 88.68 & 81.41 & 91.88 & 87.40 \\ DoubleU-Net [12] & 91.33 & 84.07 & 64.07 & 95.96 \\ LeViT-UNet [49] & 88.23 & 80.81 & 88.83 & 88.96 \\ TransUNet [48] & 89.51 & 82.10 & 90.60 & 90.02 \\ ResUNet++ [51] & 89.43 & 82.24 & 90.32 & 90.05 \\ Attention-UNet [59] & 88.79 & 81.63 & 91.81 & 87.05 \\ SSFormer-L [58] & 92.30 & 86.14 & - & - \\ EFEN & 91.65 & 84.31 & 72.43 & 96.56 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparisons on 2018 Data Science Bowl challenge.