# TTH-Net: Two-Stage Transformer-CNN Hybrid Network for Leaf Vein Segmentation
## 5 Conclusions  
In this paper, we propose a novel coarse-to-fine algorithm termed two-stage Transformer-CNN hybrid network (TTH-Net) for leaf vein segmentation task. The proposed TTH-Net consists of two stages and a cross-stage semantic enhancement (CSE) module. The first stage utilizes a transformer to obtain the global feature representations while the second stage recognizes the fine-grained features with CNN. To achieve better performance in the decoding stage, a CSE module is added to fuse the advantages of CNN and transformer, and the integrated features are fed into the decoder. In addition, the CSE module can also bridge the semantic gaps in the cross-stage features and efficiently model the long-range dependencies for further accuracy improvements. To evaluate the proposed TTH-Net, extensive experiments are conducted, and the results demonstrate that the proposed TTH-Net has the best performance among all the competitors.  
Conceptualization, P.S.; methodology, Y.Y.; data curation, Y.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, P.S.; visualization, Y.Z.; supervision, Y.Y.; project administration, P.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Tongda College of Nanjing University of Posts and Telecommunications grant No. XK006 XZ19013.  
Not applicable.  
Data Availability Statement: Data is unavailable due to privacy.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Hyperparameter\(-\lambda\)** & **Dice** & **Spe** & **Sen** & **Acc** \\ \hline
0.3 & 84.56 \(\pm\) 1.34 & 81.74 \(\pm\) 2.58 & 87.03 \(\pm\) 2.64 & 88.58 \(\pm\) 0.04 \\
**0.4** & **85.24**\(\pm\) 1.28 & **82.95**\(\pm\) 2.46 & **87.36**\(\pm\) 2.55 & **89.01**\(\pm\) 0.03 \\
0.5 & 85.07 \(\pm\) 1.31 & 82.46 \(\pm\) 2.47 & 87.10 \(\pm\) 2.57 & 88.56 \(\pm\) 0.03 \\
0.6 & 84.57 \(\pm\) 1.38 & 82.20 \(\pm\) 2.62 & 87.02 \(\pm\) 2.71 & 88.14 \(\pm\) 005 \\
0.7 & 84.42 \(\pm\) 1.35 & 82.28 \(\pm\) 2.53 & 87.04 \(\pm\) 2.69 & 88.28 \(\pm\) 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation studies of the hyperparameter \(\lambda\). Note that each metric is reported via the mean (standard deviation) of 5-fold cross-validation.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multirow{2}{*}{**Stage**} & \multicolumn{3}{c}{**Loss Function**} & \multirow{2}{*}{**Dice**} & \multirow{2}{*}{**Spe**} & \multirow{2}{*}{**Sen**} & \multirow{2}{*}{**Acc**} \\ \cline{3-4} \cline{6-9}  & & **CE** & & & & & \\ \hline \multirow{2}{*}{1} & 1 st & \(\surd\) & \(-\) & \(-\) & \(\mathbf{85.24}\)\(\pm\) 1.28 & \(\mathbf{82.95}\)\(\pm\) 2.46 & \(\mathbf{87.36}\)\(\pm\) 2.55 & \(\mathbf{89.01}\)\(\pm\) 0.03 \\  & 2 nd & \(-\) & \(\surd\) & \(-\) & \(84.56\)\(\pm\) 1.35 & \(82.37\)\(\pm\) 2.54 & \(86.47\)\(\pm\) 2.67 & 88.37 \(\pm\) 0.04 \\ \hline \multirow{2}{*}{3} & 1 st & \(\surd\) & \(-\) & \(-\) & \(\surd\) & \(83.89\)\(\pm\) 1.38 & \(82.42\)\(\pm\) 2.58 & \(85.68\)\(\pm\) 2.57 & 87.42 \(\pm\) 0.03 \\  & 2 nd & \(-\) & \(-\) & \(\surd\) & \(84.78\)\(\pm\) 1.26 & \(82.67\)\(\pm\) 2.41 & \(86.02\)\(\pm\) 2.52 & 87.76 \(\pm\) 0.04 \\ \hline \multirow{2}{*}{5} & 1 st & \(-\) & \(-\) & \(\surd\) & \(84.83\)\(\pm\) 1.29 & \(82.25\)\(\pm\) 2.44 & \(86.14\)\(\pm\) 2.41 & 88.35 \(\pm\) 0.02 \\  & 2 nd & \(\surd\) & \(-\) & \(-\) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation studies of the loss function. Note that each metric is reported via the mean (standard deviation) of 5-fold cross-validation.