Venue: MIDL 2018 Conference

Reviews:

final_score: rating: 3: Marginally above acceptance threshold
review_content: The paper addresses the problem of brain parcellation by applying existing graph based CNN techniques -GCN and GAT to parcellate areas 44 and 45 of the brain. The application of the graph based CNN methods is theoretically straight forward and hence the work lacks novelty. The results obtained by the proposed method show increase in Jaccard index compared to state-of-the-art non deep learning methods.

pros
-Application of graph based CNN on cerebral cortex parcellation
-Satisfactory evaluation of graph based CNN methods to non deep learning based methods in literature
-Good investigative analysis of various iterations of GCN and GAT methods for superior quantitative results

cons
-The paper does a poor job of explaining the experiments
-No network architecture is provided
-" For all models, we have experimented with different configurations, selecting the best architecture and optimization hyper-parameters in terms of validation Jaccard index.", "The optimized loss function is either the dice loss [7], extended to multiple classes by averaging class-specific dice losses, or a crossentropy loss, according to the best validation results."=> This is very adhoc, it is understandable it is not feasible to provide all the information, but the authors can do a better job in giving a high level idea on which different architecture/loss functions were used for what method. Was this determined by conducting only a 1 fold experiment or the determination is also based on a 10-fold cross validation? What are the different models investigated for non deep learning graph based approaches since they are based on MLP.
-For GAT training what was the reason for choosing neighbors withing 5-hops? why not 4 or 6? Does it effect performance? if so how?
-"A second limitation of the proposed cortical mesh segmentation approach is that it is currently limited to subsets of the total cortical mesh in the form of patches"=> when are the patches extracted, how are they extracted?
-All the subjects had the same mesh structure. How will the algorithm perform if that is not true?
-How significant is the improvement in accuracy from graph based CNN methods?

review_title: Application of graph based CNN to brain parcellation problem is interesting but lacks novelty in terms of the methods applied. The evaluation is satisfactory but the presented work lacks substantiation for the experimental methods used.

final_score: rating: 2: Marginally below acceptance threshold
review_content: Summary:
This paper applies existing graph-based convolutional neural network approaches to cerebral cortex parcellation study to compare their segmentation performances. NodeMLP, NodeAVG, MeshMLP, GCN, and GAT were applied to HCP dataset. Segmentation targets were two adjacent cytoarchitectonic areas 44 and 45.

Pros:
-New application of the graph-based convolutional neural network approaches is proposed. These approaches perform segmentation of areas in the cerebral cortex.
-Investigation and comparison of segmentation performances of the graph-based approaches.

Cons:
-No new method is proposed in this paper.
In the experiments, only small regions around areas 44 and 45 were processed. Performing three-classes segmentation task in the small regions seems a relatively easy task. However, the segmentation performance only achieved up to 59.7%. From this results, the evaluated graph-based segmentation approach show only limited promise to be able to segment in more broad areas on the cerebral cortex. Some questions still remain:
-Why the areas 44 and 45 were selected as segmentation target?
-Performing segmentation in small regions around areas 44 and 45 is really meaningful?
-Is it possible to segment areas in more broad areas on the cerebral cortex with reasonable segmentation performance?

Overall opinion:
While the application of graph-based convolutional neural network to brain parcellation is novel, the evaluation remains rather limited and shows only limited promise for achieving a high segmentation accuracy for more complex brain parcellation tasks.

review_title: While the application of graph-based convolutional neural network to brain parcellation is novel, the evaluation remains rather limited and shows only limited promise for achieving a high segmentation accuracy for more complex brain parcellation tasks.

final_score: rating: 4: Top 50% of accepted papers, clear accept
review_content: The authors provide a detailed and thorough investigation of graph-convolution methods for calculating a mesh of the cortical surface. 

+ the comparison of various methods is thorough and well-done. 
+ the paper reads well (although the results are not reproducible from the text, and there is no promise of making the code open) 
+ these preliminary results are promising for the chosen task. 

- the justification for only using one small area of the cortex is slightly lacking. While Broca's area may be interesting, it seems that the approach could have been tested on other areas as well -- even choosing another two bordering regions in the dataset. This would have bolstered my confidence in the method's generaliseability. 

- while analysis on the cortical surface is indeed common, the popularity of FreeSurfer is partly in its applicability to a wide variety of data, from numerous scanners/protocols. This experiment is only carried out on the highest quality data (Human Connectome Project). How would it fare under other conditions? Unfortunately, there is no attempt to use the method on another dataset (e.g. the IXI dataset: https://www.nitrc.org/projects/ixi_dataset/ ). 

- there could have been more presentation of experimental details (e.g. the architecture used), or at least an indication that the code will be released publicly. 

review_title: Graph-based CNNs work well for segmenting (a small part of) the cerebral cortex. 

Decision: Oral