## 5 Conclusions and Discussion  
The effective and efficient exploration of 3D contextual information is essential in volumetric data analysis. Although the performance of CNNs in 2D image analysis is impressive, the predictive power of its 3D generalization (i.e., 3D CNNs) is always constrained by the number of samples, especially in biomedical image analysis. Considering the conflict between the huge amount of parameters to learn in 3D CNNs and limited training samples that would quickly lead to overfitting, in this paper, we proposed a novel 3D-DSC module to replace the traditional 3D convolutional kernels. The proposed 3D-DSC module consisted of a series of densely connected 1D filters. This architecture was able to remove the redundancy within 3D kernels, while providing spaces for deepening the network and therefore could effectively reduce the risk of overfitting. In addition, inspired by the recent success of the residual network and densely connected networks, we extended the 3D separated convolution block by introducing dense connections within and between blocks. The dense connection provided an effective way to combine subsequent layers and facilitated the flow of information. Furthermore, we investigated the effect of nonlinear activation layers between the concatenated 1D filters, which had the potentiality to increase the representational power of the network and facilitate the learning of discriminative features. Experimental results on the ADHD classification and brain tumour segmentation demonstrated the superiority of the proposed 3D-DSC on volumetric image analysis. Note that 3D-DSC was not limited to any specific architecture or application and could be used to boost the performance by directly substituting the original 3D convolutional kernels.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **Depth** & **Params** & **Dice Score** \\ \hline
3D U-net & 19 & 23.5M & 0.8554 \\
3D U-net (Dropout) & 19 & 23.5 M & 0.8592 \\
3D U-net (s2-conv) & 23 & 25.9 M & 0.8593 \\
3[39] & 23 & 25.9 M & 0.8655 \\ V-net [38] & 19 & 23.5 M & 0.8685 \\
3D U-net (3D-DSC) & 28 & 17.8 M & 0.8932 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The experimental results of the proposed method and state-of-the-art methods. We trained and evaluated these methods with the same strategy on the BRATS 2017 dataset. s2, stride 2.  
Figure 7: Example segmentation results on the BRATS 2017 Challenge dataset. From left to right: the ground truth, the segmentation result of 3D U-net, the segmentation result of V-net and the segmentation result of the proposed method.  
**Author Contributions:** Formal analysis, C.W. and L.Z.; funding acquisition, L.Q. and L.Z.; methodology, L.Q. and C.W.; writing, original draft, L.Q. and C.W.; writing, review and editing, L.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the University Synergy Innovation Program of Anhui Province (GXXT-2019-008), the National Natural Science Foundation of China (61871411 and 61901003) and the Anhui Provincial Natural Science Foundation (1908085 QF255).  
**Acknowledgments:** We thank all the people involved in the study.  
**Conflicts of Interest:** The authors declare no conflict of interest.