Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: The authors present a series of graph neural networks (GNNs) trained on finite element analysis (FEA) data to model deformations of brain tissue during surgery. The authors evaluate these networks on two separate datasets, and compare against baseline FEA results. The results show that the GNN framework is capable of milisecond inference on GPU hardware, whilst still retaining a clinically feasible level of accuracy.
strengths_and_weaknesses: **Strengths**

The paper is clearly written and concise. The work presents a novel approach to modelling FEA data using a GNN whilst clearly outlining why a GNN would be a suitable architecture for this problem. The introduction and related information clearly outlines the prior work and motivations relevant to this topic. 

**Weaknesses**

The validation of the method is fairly limited, as is discussed in the limitations section 5.4. Without direct comparison to other methods data it is difficult to draw meaningful conclusions about the performance relative to similar methods.

In addition, the data used for validation is generated from finite element simulations, which is not as rigorous a source of comparison as compared to Lorente et al. (2017) which measure tissue deformations from ex-vivo data.
questions: 1. In section 5.1, you quote the time taken for the model to produce predictions on a GPU & CPU. Do you have the time taken to produce the  results using the original baseline data (i.e. the calculated FEA results)? Including this, as well as a quote of the speedup factor would be very useful as one of the main motivations of the model is the relative compute efficiency boost you would gain from this method.

2. You mention in section 5.4 that you lacked "accessibility to other studies’ data and/or implementation". Did you attempt to contact the authors to get access to the aforementioned datasets & implementations to validate your method against theirs?

3. You also mentioned in section 5.4 that in some cases you lacked "sufficient computational power to use their data/models". Could you elaborate on this please?
limitations: The authors appropriately address the limitations of the study, including the lack of validation datasets present within other studies, and the inherent limitation of using FEA as a baseline.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: In this paper, the authors present a method for soft tissue deformation of the brain using GNNs. The GNN is trained with the results of a FEM simulation. Compared to other methods that were trained on FEM results, the authors use a GNN to account for the graph structure of the FEM. The results show that the presented method is on par with other methods for soft tissue deformation of the brain.
strengths_and_weaknesses: The paper is very well written and organized, especially the introduction and the prior work section is very detailed. The method part is missing some details and is difficult to understand. For example, the description (177 – 186) of Eq. 1 and 2. Having a detailed description of each element of Eq. 1 and 2 would improve the understanding. The evaluation could be more detailed as only the mean and std error is provided. A more detailed evaluation of the different regions of the brain would be beneficial.
questions: 1) Is there a specific reason to use two different aggregation functions (mean and max)?
2) Are the node features that are used as input the same for all nodes?
3) Why is the input provided in polar and Cartesian coordinates?
4) Is the output of the last linear layer 63x3? 

limitations: Yes, the authors have addressed the limitations of their work.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: In this paper, the authors present the creation of a surrogate FEM model of the biomechanical behavior of the brain. The creation of this surrogated model is based on the use of Graph Neural Network (GNN) for its ability to allow introducing not only the positions of the nodes but also the connections between them (by sending messages). Compared to similar approaches, the model is competitive in terms of speed and accuracy. In addition, the authors state that their approach allows the generation of models surrogated to different approximations by means of finite elements. 
strengths_and_weaknesses: Strengths:
*.- This approximations allow to generate fast and accurate surrogate models to FEM based on GNN. 
*.- Due to the use of GNN, it can consider connexions between nodes. 
*.- It is general enough to be applied to other Finite Element approximations. 

Weaknesses:
*.- In this approximation (as other ones) only forces are considerer when (f.e. in case of brain-shift) most of the time only the displacement of the nodes can be obtained. 
*.- It is only compared to synthetic models.
questions: *.- How could this approximation be applied in real scenarios?
*.- Could it be applied to the simulation of anisotropic tissue behavior? 
*.- The images are a little bit poor. They should be improved.

limitations: Authors have adequately addressed the limitations of their approach.
review_title: null

Meta-review: metareview: The paper proposes a method for modeling soft tissue deformations using a graph neural network trained to approximate the solution of the more classic finite element method. The proposed method is significantly faster than the classic method and with sufficient accuracy, enabling the prospect of using the method to predict tissue deformation in image-guided neurosurgery.

All reviewers appreciated the novelty and utility of the proposed method. The biggest weakness in the study had to do with the limited validation performed. This limitation was clearly addressed by the authors both in the limitations section and in their response to the reviewers. Despite the limitation, the reviewers unanimously recommend acceptance.
confidence: Certain
Decision: Accept