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Once again, thank you for submitting your manuscript to International Journal of Imaging Systems and Technology and I look forward to receiving your revision.
Dr Mohamed Seghier
Editor, International Journal of Imaging Systems and Technology
Referee(s)' Comments to Author:
Comments to the Author
1- Some important and novel techniquse are missing in the literature part.
-Gu, Z., Cheng, J., Fu, H., Zhou, K., Hao, H., Zhao, Y., ... & Liu, J. (2019). CE-Net: Context Encoder Network for 2D Medical Image Segmentation. IEEE transactions on medical imaging.
-Öztürk S¸, Akdemir B. Celltype based semantic segmentation of histopathological images using deep convolutional neural networks. Int J Imaging Syst Technol. 2019;1 13. https://doi.org/10.1002/ima.22309
2- The most important contribution of this study is upsampling of feture maps. Authors have to show this at the figure 1. Figure 1 is unclear
3- What is the motivation of Xcception block? What is the formulation of Xception block?
4- Section 2.2 has to be explained detailed.
5- Section 2.3 has to be explained detailed. (Authors has to explain their parameters, their contribution, the different part of their methods in Section 2.1, 2.2, 2.3)
6- Section 2.4 is not clear. Authors has to add figure.
7- TP, FN,FP explanations are generic. Authors have to explained these parameters according to their data.
8- Further research on the effects of network depth should be done and the results should be added.
9- Computational complexity should be added.
10- Visual comparison results should be added as a new figure.
11- More state-of-the-art algorithm results can be added.
Comments to the Author
This paper conducted gliomas segmentation using multi-scale 3D Unet. To enhance the accuracy of segmentation masks, multi-scale features, 3D U-net with separable convolution block, and multi-pooling blocks were utilized. Performance was validated by comparison to multiple previous techniques using Dice, sensitivity, and PPV.
The manuscript is well written. But, I have a few inquiries and suggestions to improve this manuscript.
1. Table 2 shows the improvement of segmentation masks with respect to each component of your network, such as the convolution block, and pooling. Can you show the differences of segmentation masks according to each component?
2. Table 3 shows that the performance of the proposed method is less accurate than other methods. Can you explain other advantages of your technique (ex. number of parameters or FLOPs) in result and discussion section?
3. Table 3 shows that the proposed method can produce an accurate segmentation mask in whole but not in core and Enh. Can you discuss the reason for these results?