Paper: MGDC-UNet: Multi-group Deformable Convolution for Medical Image Segmentation (ICLR-2024)

Reviewer comment (Reproducibility):
More experimental details should be provided to facilitate replication of the proposed method as well as a fair assessment of the performance of all comparison methods, e.g. batch size, learning rate, loss function, etc.

Author response:
* Thank you for your insightful comment regarding the need for more detailed experimental information in our paper. 
We would like to clarify that our training recipes including batch size, learning rate, loss function, and other relevant parameters, were indeed provided in our first submission (Table 5 of the Appendix). However, we recognize that this information might not have been immediately apparent or easily accessible to readers. To address this and improve the clarity of our manuscript, we have made the following modification: 
  * We updated the manuscript with more detailed explanation of hyperparameters used in training MGDC-UNet in **Appendix A.2**. We provided information such as batch size, loss function, network depths, channels and channel groups etc. 
  * We have included an explicit reference to **Appendix A.2.** in the main text of the paper, specifically in the section 4.1 discussing the implementation details.

Reviewer comment (Experiments):
The authors should have given more details about the data strategy, how the training dataset, validation set, and test set were divided, and how the optimal model was selected for performance comparison.

Author response:
* We thank you for your suggestion. In the original submission, we mentioned the use of a 5-fold cross-validation procedure to ensure the robustness and generalizability of our results. To further clarify this process, we have now included these information in **Appendix A.1** of our revised manuscript. This table details the exact distribution of the training, validation, and test sets for each dataset used in our study. For choosing the best model for evaluation, we used earlystopped the best model on validation performance. We also included vital information such as input image modality and evaluation metrics.

Reviewer comment (Experiments):
The datasets utilized in the experiments of the paper all feature objects with clear boundaries that are easy to segment. Given the authors' claim that their method can better capture complex pathological features, for a more comprehensive evaluation of the algorithm's performance, it would be advisable for the authors to attempt performance assessment on datasets with complex pathological and morphological characteristics, such as retinal OCT image segmentation, for example the dataset of RETOUCH - The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge.

Author response:
* Thank you for your your suggestion to evaluate our MGDC-UNet on datasets with more complex pathological and morphological characteristics, such as the RETOUCH. We recognize the importance of testing segmentation algorithms on a variety of datasets to ensure robustness and versatility.

* Firstly, we would like to point out that our submission already includes comprehensive experiments on three large datasets (BraTS, FLARE, AMOS) that are well-recognized benchmarks for 3D medical image segmentation. These datasets have been utilized extensively in the literature, including in works such as UXNET [2], MedNEXT [3] and SwinUNETR [4] to evaluate the performance of segmentation methods. While the boundaries in these datasets may appear clear, the task of segmenting medical images accurately is nonetheless challenging and essential for clinical impact. 
* We have made efforts to include additional datasets in our assessment. While we attempted to access the RETOUCH challenge dataset to extend our evaluation to retinal OCT images, unfortunately, we did not receive a response to our request, possibly due to the challenge already having concluded. This has limited our ability to perform the recommended analysis.

* Nonetheless, to address your concern, **we have conducted additional experiments on the Prostate158 [5] dataset with the task of segmenting prostate gland and clinically significant prostate cancer.** Accurately segmenting the prostate and prostate cancer from MRI is difficult due to unclear boundaries with adjacent tissues and heterogeneous textures of the cancerous regions [6]. 

* We have updated the manuscript to include results from Prostate158 (**Appendix A.3**). Our experiments still confirmed that MGDC-UNet maintains excellent performance, even on this smaller and more challenging benchmark. 
* For prostate region segmentation, small kernel MGDC-UNet (k=3) significantly outperformed all comparable methods, achieving a DSC of 0.855 and HD95 of 5.06. Further increasing kernel size to 5 led to 1.0% percent improvement in DSC and best performance was achieved at k=7, with 0.866 DSC. 
* For prostate cancer segmentation, the small kernel MGDC-UNet (k=3) outperformed other state-of-the-art methods, achieving a DSC of 0.515 and HD95 of 7.77. Increasing kernel size to 5 and 7 further led to 0.9% and 1.2% improvement in DSC. We believed that larger MGDC kernels capture a broader spatial context, which is beneficial for difficult segmentation tasks involving irregular and heterogeneous objects such as prostate gland and clinically significant prostate cancer. These results reinforce the capability of MGDC-UNet to capture complex pathological features beyond those present in the datasets initially used for evaluation.

* In closing, we appreciate the opportunity to further substantiate the performance of MGDC-UNet and thank you for the suggestion to explore additional datasets. We believe that our method's strong performance across multiple benchmarks, including those with complex pathological features, attests to its robustness and effectiveness in medical image segmentation.

Reviewer comment (Novelty):
The originality of the algorithm's contribution remains ambiguous. Both deformable convolution and MLP are well-established application techniques, and while the authors have successfully combined them to achieve improved performance, their original contribution appears relatively modest.

Author response:
* Thank you for raising concerns about the originality of our MGDC-UNET algorithm. We first highlighted our novelties as below:
  * **Innovative MGDC operator**: Our primary contribution lies in the unique combination of deformable convolution with a multi-group mechanism, distinct from traditional deformable convolution approaches. Unlike standard DCN, MGDC addressed the overparameterized process of computing offsets and masks by using depth-wise convolution, significantly reducing parameter counts without compromising the network's ability to learn complex features. Furthermore, our multi-group mechanism, inspired by successful models like ResNeXt [1], divides feature dimensions into groups, which compute deformable masks and offsets independently, facilitating diverse and intricate feature learning.
  * **Transformer Components for Advanced Feature Learning**: The efficiency of our MGDC operator allows us to integrate more complex components, MLP and LayerNorm, into our MGDC-UNet architecture to advance feature learning capabilities. We found that this integration did not introduce excessive parameter burdens, while significantly boosting the model's performance as seen in our ablation studies. We believe that this careful coordination of our operator and architecture design not only optimizes computational efficiency but also significantly enhances the model's capacity for accurate and intricate segmentation in medical imaging.
  * **Insight into Kernel Size Exploration**: Another aspect of our contribution is the exploration of different kernel sizes to optimize the Effective Receptive Field (ERF). This approach is particularly relevant in medical imaging, where capturing intricate spatial relationships is paramount (as seen in Figure 1). Our experiments with various kernel sizes demonstrate that enlarging the ERF can indeed enhance the model's segmentation performance, a finding critical to the advancement of medical image segmentation techniques.

* In summary, our MGDC-UNET combines an innovative MGDC operator to reduce parameters and facilitate complex feature learning by integrating transformer elements. Additionally, the exploration of various kernel sizes optimizes the ERFs, crucial for capturing detailed spatial relationships in medical imaging, thus significantly improving segmentation accuracy. Our work represents a significant step forward in the field, offering a novel and effective solution for complex segmentation tasks.

Reviewer comment (Reproducibility):
More experimental details should be provided to facilitate replication of the proposed method as well as a fair assessment of the performance of all comparison methods, e.g. batch size, learning rate, loss function, etc.

Author response:
* Thank you for your insightful comment regarding the need for more detailed experimental information in our paper. 
We would like to clarify that our training recipes including batch size, learning rate, loss function, and other relevant parameters, were indeed provided in our first submission (Table 5 of the Appendix). However, we recognize that this information might not have been immediately apparent or easily accessible to readers. To address this and improve the clarity of our manuscript, we have made the following modification: 
  * We updated the manuscript with more detailed explanation of hyperparameters used in training MGDC-UNet in **Appendix A.2**. We provided information such as batch size, loss function, network depths, channels and channel groups etc. 
  * We have included an explicit reference to **Appendix A.2.** in the main text of the paper, specifically in the section 4.1 discussing the implementation details.

Reviewer comment (Experiments):
The authors should have given more details about the data strategy, how the training dataset, validation set, and test set were divided, and how the optimal model was selected for performance comparison.

Author response:
* We thank you for your suggestion. In the original submission, we mentioned the use of a 5-fold cross-validation procedure to ensure the robustness and generalizability of our results. To further clarify this process, we have now included these information in **Appendix A.1** of our revised manuscript. This table details the exact distribution of the training, validation, and test sets for each dataset used in our study. For choosing the best model for evaluation, we used earlystopped the best model on validation performance. We also included vital information such as input image modality and evaluation metrics.

Reviewer comment (Experiments):
The datasets utilized in the experiments of the paper all feature objects with clear boundaries that are easy to segment. Given the authors' claim that their method can better capture complex pathological features, for a more comprehensive evaluation of the algorithm's performance, it would be advisable for the authors to attempt performance assessment on datasets with complex pathological and morphological characteristics, such as retinal OCT image segmentation, for example the dataset of RETOUCH - The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge.

Author response:
* Thank you for your your suggestion to evaluate our MGDC-UNet on datasets with more complex pathological and morphological characteristics, such as the RETOUCH. We recognize the importance of testing segmentation algorithms on a variety of datasets to ensure robustness and versatility.

* Firstly, we would like to point out that our submission already includes comprehensive experiments on three large datasets (BraTS, FLARE, AMOS) that are well-recognized benchmarks for 3D medical image segmentation. These datasets have been utilized extensively in the literature, including in works such as UXNET [2], MedNEXT [3] and SwinUNETR [4] to evaluate the performance of segmentation methods. While the boundaries in these datasets may appear clear, the task of segmenting medical images accurately is nonetheless challenging and essential for clinical impact. 
* We have made efforts to include additional datasets in our assessment. While we attempted to access the RETOUCH challenge dataset to extend our evaluation to retinal OCT images, unfortunately, we did not receive a response to our request, possibly due to the challenge already having concluded. This has limited our ability to perform the recommended analysis.

* Nonetheless, to address your concern, **we have conducted additional experiments on the Prostate158 [5] dataset with the task of segmenting prostate gland and clinically significant prostate cancer.** Accurately segmenting the prostate and prostate cancer from MRI is difficult due to unclear boundaries with adjacent tissues and heterogeneous textures of the cancerous regions [6]. 

* We have updated the manuscript to include results from Prostate158 (**Appendix A.3**). Our experiments still confirmed that MGDC-UNet maintains excellent performance, even on this smaller and more challenging benchmark. 
* For prostate region segmentation, small kernel MGDC-UNet (k=3) significantly outperformed all comparable methods, achieving a DSC of 0.855 and HD95 of 5.06. Further increasing kernel size to 5 led to 1.0% percent improvement in DSC and best performance was achieved at k=7, with 0.866 DSC. 
* For prostate cancer segmentation, the small kernel MGDC-UNet (k=3) outperformed other state-of-the-art methods, achieving a DSC of 0.515 and HD95 of 7.77. Increasing kernel size to 5 and 7 further led to 0.9% and 1.2% improvement in DSC. We believed that larger MGDC kernels capture a broader spatial context, which is beneficial for difficult segmentation tasks involving irregular and heterogeneous objects such as prostate gland and clinically significant prostate cancer. These results reinforce the capability of MGDC-UNet to capture complex pathological features beyond those present in the datasets initially used for evaluation.

* In closing, we appreciate the opportunity to further substantiate the performance of MGDC-UNet and thank you for the suggestion to explore additional datasets. We believe that our method's strong performance across multiple benchmarks, including those with complex pathological features, attests to its robustness and effectiveness in medical image segmentation.

Reviewer comment (Novelty):
The novelty of the paper is very limited as the method is a combination of many widely used techniques. Specifically, on page 5, the authors claim to develop a new architecture that includes a reverse bottleneck; however, this design is just similar to the inverted residual block used in MobileNetV2 [1].

Author response:
> **The novelty of the paper is very limited as the method is a combination of many widely used techniques. Specifically, on page 5, the authors claim to develop a new architecture that includes a reverse bottleneck; however, this design is just similar to the inverted residual block used in MobileNetV2 [1].**
* While we acknowledge the similarity of our MGDC block's inverted bottleneck design to the ones in MobileNetV2, we wish to highlight two novel aspects that distinguishes MGDC block from MobileNetV2 block:
  * **1). Use of transformer components**: Our architecture’s uniqueness stems not just from the inverted bottleneck design but also from **the incorporation of MLP networks and LayerNorm**—elements not used in MobileNetV2. These components, inspired by ViTs, play a pivotal role in enhancing the model's performance, validated in our ablation studies (+ 0.5% DSC in BraTS and +0.8% DSC in FLARE21). 
  * **2). Use of multi-group deformable convolution**: Our primary contribution lies in the unique combination of deformable convolution with a multi-group mechanism, distinct from traditional DCNs. MGDC addressed the overparameterized process of computing offsets and masks by using depth-wise convolution, significantly reducing parameter counts without compromising the network's capacity. With our novel MGDC, our network can effectively enlarge the effective receptive fields (ERFs) to handle the complex spatial relationships in 3D medical images (Figure 1).
* To clarify this in our manuscript, we propose revising the original sentence in page 5 as follows:
*We present the MGDC Block, a new architecture that includes a reverse bottleneck design similar to MobileNetV2, but augmented with transformer components. While traditional inverted bottleneck design utilized depthwise convolution, our MGDC block leverages two MLP layers for channel expansion and reduction and LayerNorm for normalization, a design further inspired by Vision Transformers.*

Reviewer comment (Novelty):
On page 2, the authors mention that they have developed a deformable convolution approach for 3D volumetric images, however, 3D deformable convolution has been applied before for videos in [2].

Author response:
> **Also, the authors mention on page 2 that they have developed a deformable convolution approach for 3D volumetric images, however, 3D deformable convolution has been applied before for videos in [2].**

* While we acknowledge that both our method and Ying et al’s method (D3D) used 3D deformable convolution, we highlight two important distinctions between MGDC and D3D:
  * **1). How offset was computed for deformable convolution kernels**: D3D was developed specifically for 3D temporal data, which composed of a series of 2D images at different time scales. Hence, Ying et al. designed D3D for *super resolution task and only perform kernel deformation in spatial dimension to incorporate the temporal prior (i.e., frames temporally closer to the reference frame are more important) and reduce computational cost* [2]. Given the input feature of size C×T×W×H, D3D first computes offsets in size of 2N×T×W×H. Note that, the number of channels of these offset features is set to 2N for 2D spatial deformations (i.e., deformed along height and width dimensions). Then, the learned offsets are used to guide the deformation in a 2D sampling grid ( i.e., W and H dimensions), which produce the output feature map of D3D. 
  * **In contrast, our MGDC operator is specifically designed for 3D volumetric medical images**. Hence, MGDC computes offsets for 3D spatial deformations ( i.e., depth, width and height dimensions) to also account for the depth dimension characteristic of 3D medical images. Given the input feature of size C×T×W×H, our MGDC will compute offsets in size of 3N×T×W×H to guide the deformation in a 3D sampling grid, which eventually produce the output features of MGDC. 

  * **2). MGDC improves traditional DCN using depthwise convolution and multi-group mechanisms to decrease parameter counts**: Existing DCN methods such as D3D are not parameter efficient in computing feature maps, which only used plain convolutions. MGDC addresses this by adopting depth-wise convolution to reduce parameter counts. Also, we further incorporated multi-group mechanisms (as seen in ResNeXt) into DCN when computing offsets and modulation scalars. This allows our network to learn diverse and complex features more effectively. As shown in Table 4, adding multi-group mechanisms slightly reduced parameters while improving segmentation performances on both datasets. 
* Finally, to fully address your concern regarding the effectiveness of MGDC vs D3D, we added the following experiments comparing the two operators on BRATS 21:
|Operator|DSC|HD95|
|--|--|--|
| D3D  | 0.892 | 7.249|
| MGDC (k=3)| 0.906| 4.816|

* We observed that D3D did not outperform MGDC, which we attributed to the core differences in how each algorithm was designed for each task. In summary, MGDC-UNet is distinctively tailored for 3D volumetric medical images with 3D spatial deformations and improved parameter efficiency.

Reviewer comment (Theory):
On page 3, the authors claim that their method is improving computational efficiency, however, the only component that seems to decrease complexity is the depth-wise convolution. Following that, many components are added such as linear layers, SoftMax, transformer components, and an extra loop over “groups” which adds to the complexity of the network.

Author response:
> **Moreover, the authors claim on page 3 that their method is improving computational efficiency, however, the only component that seems to decrease complexity is the depth-wise convolution. Following that, many components are added such as linear layers, SoftMax, transformer components, and an extra loop over “groups” which adds to the complexity of the network.**
* Thank you for your insightful observations regarding the computational efficiency of our network. Here, we summarize the role of each component in MGDC block:
  * 1). **Depth-wise Convolution**: As you rightly noted, the incorporation of depth-wise convolution is a key factor in reducing the network's parameters when computing offsets. As shown in Table 4 (row 1 and 2), introducing a shared weight mechanism to MGDC **decreased 22% parameters (from 71.5 millions to 58.7 millions)**. 
  * 2). **Multi-group mechanism**: Motivated by ResNeXt, we utilized group convolution to further improve the efficiency of deformable convolution. Instead of an “extra loop over groups”, our MGDC splits the feature dimensions into G groups, each of which computes its own deformable masks and deformable offsets. Compared to plain convolution in which every output channel is connected to every input channel, group convolution reduces computational load since each output channel is only connected to a subset of input channels (those within its group). As shown in Table 4 (row 3), adding multi-group mechanisms **slightly reduced the parameters by 0.6M** while improving DSC on both datasets (0.5% on BraTS21 and 0.2% on FLARE21). 
  * 3). **Linear Layers**: Similar to prior works in DCNv2 [3], our MGDC also utilized linear layers to compute offsets and modulation scalars (masks). In our method section, we compared our MGDC to a 3D implementation of DCN which also utilized linear layers to compute offsets and masks (not shown in manuscript due to page limit). Therefore, we respectively point out that this can hardly be considered as adding additional complexities to our network. For reference, we also attached github for DCNv2 implementation: https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/7e69911acfca4183172b829c50b174af721310e8/modules/modulated_dcn.py#L144

  * 4). **Softmax**: To stabilize training, SoftMax was used to normalize mask values so that they sum up to 1 across each group. This also allows adaptive weighting the importance of different groups of the feature maps, similar to multi-head self-attention. Since softmax function contains no learnable parameters, it doesn't contribute to the model's parameter count. Also, SoftMax only involves exponentiation and normalization of input values, which are not computationally intensive. Therefore, we believed that the inclusion of a SoftMax layer in a model does not add significant computational complexity. 

  * 5). **Transformer components**: While we acknowledge that incorporating transformer components indeed added extra parameters to our network, our motivation for adding such components is not to improve computational efficiency, **but to improve feature learning capabilities.** Motivated by ViT blocks, we integrated MLP layers and LayerNorm into our MGDC block to perform feature expansion and reduction (inverted bottleneck) design. As validated by our ablation studies, incorporating MLPs improved DSC by 0.5% in BRATS21 and by 0.8% in FLARE21. What’s more, we kindly note that even after introducing MLPs to MGDC, the parameter only increased to 61 M, which is still **15%** less than the original 3D DCN network. 

* To improve the clarity of the baseline 3D DCN method which we compared to, we modified the sentence in method 3.1 describing 3D DCN to: *Accordingly, we first take a 3D a dynamic deformable convolution network (3D DCN) (Zhu et al.
(2019)) with adaptive sampling offsets and modulation masks to enhance the targeted segmentation
tasks.*

Reviewer comment (Evaluation):
Tables 1 and 2 suggest that there is not much improvement in time and memory usage as other methods are comparable in performance (SegResNet inference time is 0.78 with memory usage of 3.3G while MGDC-UNET has inference time of 2.08 with 9.4G memory).

Author response:
> **Tables 1 and 2 suggest that there is not much improvement in time and memory usage as other methods are comparable in performance (SegResNet inference time is 0.78 with memory usage of 3.3G while MGDC-UNET has inference time of 2.08 with 9.4G memory). Time and memory usage were not mentioned in Table 3 which is inconsistent with the previous tables.**
* Thank you for your comments regarding the time and memory usage comparisons in Tables 1. While it is true that SegResNet is more efficient in these aspects, we note that MGDC-UNet **significantly outperforms SegResNet in terms of segmentation accuracy**, with an improvement of 1.1% in DSC and a reduction of 2.64 mm in HD95. In clinical applications, where segmentation accuracy is paramount, the superior performance of MGDC-UNet could be more beneficial, despite its higher computational requirements.
* Furthermore, when considering the BRATS21 dataset, the best SOTA method is SwinUNETR. We note that MGDC-UNet not only surpasses SwinUNETR in segmentation accuracy but also demonstrates a 35% improvement in inference time and a 27% reduction in memory consumption. This comparison underscores MGDC-UNet's efficiency and effectiveness, particularly in a challenging dataset like BRATS21.
* Regarding the absence of time/memory analysis for the FLARE21 and AMOS22 datasets in Table 2 and 3, this was primarily due to page limitations. However, to provide a comprehensive understanding, we have included the time and memory usage details for these datasets in **Appendix A.5 Table 8**. To provide a wholistic understanding of the benefits of our method, we have also included the performance gaps of each method to our MGDC-UNet (k=3). From Table 8, we observed that while SegResNet indeed demonstrated faster inference speed and less memory usage, its segmentation performance is not comparable to MGDC-UNet.

Reviewer comment (Presentation):
Time and memory usage were not mentioned in Table 3 which is inconsistent with the previous tables.

Author response:
> **Tables 1 and 2 suggest that there is not much improvement in time and memory usage as other methods are comparable in performance (SegResNet inference time is 0.78 with memory usage of 3.3G while MGDC-UNET has inference time of 2.08 with 9.4G memory). Time and memory usage were not mentioned in Table 3 which is inconsistent with the previous tables.**
* Thank you for your comments regarding the time and memory usage comparisons in Tables 1. While it is true that SegResNet is more efficient in these aspects, we note that MGDC-UNet **significantly outperforms SegResNet in terms of segmentation accuracy**, with an improvement of 1.1% in DSC and a reduction of 2.64 mm in HD95. In clinical applications, where segmentation accuracy is paramount, the superior performance of MGDC-UNet could be more beneficial, despite its higher computational requirements.
* Furthermore, when considering the BRATS21 dataset, the best SOTA method is SwinUNETR. We note that MGDC-UNet not only surpasses SwinUNETR in segmentation accuracy but also demonstrates a 35% improvement in inference time and a 27% reduction in memory consumption. This comparison underscores MGDC-UNet's efficiency and effectiveness, particularly in a challenging dataset like BRATS21.
* Regarding the absence of time/memory analysis for the FLARE21 and AMOS22 datasets in Table 2 and 3, this was primarily due to page limitations. However, to provide a comprehensive understanding, we have included the time and memory usage details for these datasets in **Appendix A.5 Table 8**. To provide a wholistic understanding of the benefits of our method, we have also included the performance gaps of each method to our MGDC-UNet (k=3). From Table 8, we observed that while SegResNet indeed demonstrated faster inference speed and less memory usage, its segmentation performance is not comparable to MGDC-UNet.

Reviewer comment (Evaluation):
In Figure 1, self-attention is shown to be comparable to the proposed MGDC. Figure 3 suggests a very small improvement but not significant. Similarly, in Figures 4 and 5, the improvement does not seem significant.

Author response:
> **In Figure 1, we can clearly see that self-attention is comparable to the proposed MGDC. Figure 3 suggests a very small improvement but not significant. Similarly, in Figures 4 and 5, the improvement does not seem significant.**
* We thank you for bringing these to our attention. We have updated Figure 1 to provide a clearer visual representation of the advantages offered by multi-group deformable convolutions. From the top row, we observed that MGDC effectively enlarges ERFs while adapting to the geometries of various organs due to the learnable offsets. In comparison, while self-attention methods and large kernel methods both improved ERFs, they could not adequately capture the complex semantic relationships among correlated organs. In the second row, MGDC-UNet demonstrated best segmentation performance since its ERF is suitably expanded to encompass correlated organs.
* For your concerns in Figure 3, 4 and 5, we also revised them include both cases with large improvements and cases with moderate improvement. Each row represents a distinct case, arranged in descending order of MGDC-UNET's relative improvement over other methods, with **the top row** depicting the most significant enhancement.
* For figure 3 (first and second rows), our MGDC-UNet effectively minimizes false positive NCR (red) and ET (yellow) regions compared to competing methods. To maintain a balanced and fair comparison, the third row showcased MGDC-UNet exhibited moderate differences in tumor segmentation. For figure 4, the top row depicted MGDC-UNet effectively segmented pancreas (red) whereas other methods failed. For figure 5, the top row depicted MGDC-UNet effectively segmented stomach (green) whereas other methods failed.

Reviewer comment (Evaluation):
On page 6, the authors claim that increasing kernel size from 3 to 7 causes enhancements, however, some results in Table 2 disprove this claim.

Author response:
> **On page 6, the authors claim that increasing kernel size from 3 to 7 causes enhancements, however, some results in Table 2 disprove this claim.**
* We acknowledge that in Table 2, increasing the kernel size did not uniformly improve DSC for the liver and pancreas. However, we found that **gradually enlarging the kernel size leads to increasing number of metrics being statistically significant against the best SOTA methods across all datasets**. For example, in Table 2, we found that enlarging kernel size from 3 to 5 significantly improved DSC on spleen. Further improving kernel from 5 to 7 significantly improved DSC on kidney, liver and SDC on spleen and on average. 
* Regarding your concern for Table 2, the absence of significant improvement for the liver at larger kernel sizes could be attributed to the network's performance nearing saturation, particularly as it was already achieving a high accuracy of 99.4%. We also found both average DSC and SDC showed slight increases when transitioning from smaller to larger kernels. 
* Our claim is substantiated by growing statistically significant metrics outperforming SOTA models and a general upward trend in average metrics. Despite some less notable improvements, the overall data confirms that larger kernel sizes notably enhance segmentation results in MGDC-UNET.