{"forum": "HyemZQzExE", "submission_url": "https://openreview.net/forum?id=HyemZQzExE", "submission_content": {"title": "CT-To-MR Conditional Generative Adversarial Networks for Improved Stroke Lesion Segmentation", "authors": ["Jonathan Rubin", "S. Mazdak Abulnaga"], "authorids": ["jonathan.rubin@philips.com", "abulnaga@mit.edu"], "keywords": ["Conditional adversarial networks", "Image-to-Image translation", "Ischemic stroke lesion segmentation", "CT perfusion"], "TL;DR": "CT-To-MR Conditional GAN for Stroke Lesion Segmentation", "abstract": "Infarcted brain tissue resulting from acute stroke readily shows up as hyperintense regions within diffusion-weighted magnetic resonance imaging (DWI). It has also been proposed that computed tomography perfusion (CTP) could alternatively be used to triage stroke patients, given improvements in speed and availability, as well as reduced cost. However, CTP has a lower signal to noise ratio compared to MR. In this work, we investigate whether a conditional mapping can be learned by a generative adversarial network to map CTP inputs to generated MR DWI that more clearly delineates hyperintense regions due to ischemic stroke. We detail the architectures of the generator and discriminator and describe the training process used to perform image-to-image translation from multi-modal CT perfusion maps to diffusion weighted MR outputs. We evaluate the results both qualitatively by visual comparison of generated MR to ground truth, as well as quantitatively by training fully convolutional neural networks that make use of generated MR data inputs to perform ischemic stroke lesion segmentation. We show that segmentation networks trained with generated CT-to-MR inputs are able to outperform networks that make use of only CT perfusion input.", "pdf": "/pdf/770e81239d2141fa71de6de5df3e068cf1a138cd.pdf", "code of conduct": "I have read and accept the code of conduct.", "paperhash": "rubin|cttomr_conditional_generative_adversarial_networks_for_improved_stroke_lesion_segmentation"}, "submission_cdate": 1544983290621, "submission_tcdate": 1544983290621, "submission_tmdate": 1545069827937, "submission_ddate": null, "review_id": ["H1edxZ3FzE", "rJl8HF3nXN", "BylbWfbpm4"], "review_url": ["https://openreview.net/forum?id=HyemZQzExE&noteId=H1edxZ3FzE", "https://openreview.net/forum?id=HyemZQzExE&noteId=rJl8HF3nXN", "https://openreview.net/forum?id=HyemZQzExE&noteId=BylbWfbpm4"], "review_cdate": [1547448560260, 1548695869911, 1548714489206], "review_tcdate": [1547448560260, 1548695869911, 1548714489206], "review_tmdate": [1548856705092, 1548856699260, 1548856692768], "review_readers": [["everyone"], ["everyone"], ["everyone"]], "review_writers": [["MIDL.io/2019/Conference/Paper144/AnonReviewer3"], ["MIDL.io/2019/Conference/Paper144/AnonReviewer1"], ["MIDL.io/2019/Conference/Paper144/AnonReviewer2"]], "review_reply_count": [{"replyCount": 0}, {"replyCount": 0}, {"replyCount": 0}], "review_replyto": ["HyemZQzExE", "HyemZQzExE", "HyemZQzExE"], "review_content": [{"pros": "To investigate whether a conditional mapping can be learned by a generative adversarial network to map CTP inputs to generated MR DWI that more clearly delineates hyperintense regions due to ischemic stroke.\nTo perform image-to-image translation from multi-modal CT perfusion maps to di\u000busion weighted MR outputs\nTo make use of generated MR data inputs to perform ischemic stroke lesion segmentation.", "cons": "There is no detail on qualitatively visual comparison of generated MR to ground truth.\nThe authors had better compare segmentation result between CTP with orginal MRI and CTP with CGAN MRI.\nThe gain using CGAN MRI looks marginal, which would be better to apply ablation study.\n", "rating": "2: reject", "confidence": "2: The reviewer is fairly confident that the evaluation is correct"}, {"pros": "This work proposes an image-to-image translation approach for improving lesion segmentation, in the scenario when time and cost limitations allow for acquisition of only CT perfusion images. Overall, the article is well-motivated and clearly written.", "cons": "1. This work applies a known technique (image-to-image translation using paired training data) to a new problem (CT perfusion to MRI for lesion segmentation), but the experiments demonstrate only marginal improvement, unfortunately. Thus, despite being a promising start, I believe that this work is not ready for publication at this stage.\n\n2. As the mean values of all metric show only marginal improvements and qualitative results in Fig. 3 show some samples with much better results for the FCN-CGAN, it would seem that there are other cases for which segmentation with the FCN performs much better. Is this the case? If so, it would make sense to show some examples of this type and mention this as a limitation. Also, in this case, the sentence 'The results show that, in general, the FCN-CGAN model results in predictions that cover more of the ischemic core region...' might be misleading.\n\n3. A benchmark experiment, where CT perfusion and real MR images are used for segmentation, should be added.\n\nMinor:\n4. As the methods have been compared with several metrics, a discussion about how the different metrics compare with one another might be suitable.\n\n5. The related work section requires some restructuring, in my opinion. Perhaps, the authors could consider a higher level of abstraction such as 'image-to-image translation for downstream tasks', 'image-to-image translation for data augmentation', etc. Also, details about some of the mentioned works that are perhaps irrelevant for the proposed work (e.g. 'Heavier weighting of the L1 loss around the border...') could be skipped.\n\n6. A suggestion to the authors: perhaps optimizing the MR image generation such that it leads to good segmentation results might lead to improved segmentation results?\n\n7. Do the authors mean to number Sec 4.4 and Sec 4.5 as Sec 4.3.1 and Sec 4.3.2 respectively?", "rating": "2: reject", "confidence": "2: The reviewer is fairly confident that the evaluation is correct"}, {"pros": "The paper describes a method employing conditional generative adversarial networks to aid the stroke lesion segmentation on CT perfusion images. The paper is well-written and clearly structured. The clinical application is well-motivated. ", "cons": "The major problem is the presented results. With 94 pairs of data from 63 subjects, the statistical significance of the claimed improvement from Table 1 seems questionable. For instance, a difference of 0.06 in Hausdorff Distance, which is known for being with high variance, is unlikely to be significant given reported standard deviation being around ~20. This continues with other metrics, which suggested that the visually superior results in Figure 3 can be highly selective and therefore misleading.  \n\nA few minor comments include: 1) fairly limited technical contribution to improve the results, e.g. why 3D network was not adopted and tested while 2D formulation maybe efficient but loses 3D \"convolutional constraint\"; 2) no effort has been made to network adaptation, e.g. hyper-parameters from other unrelated applications, to the application - which itself may not be a problem and may be problematic within cross-validation. However, given the presented results, this became relevant and needs a better experiment strategy for future work.", "rating": "2: reject", "confidence": "2: The reviewer is fairly confident that the evaluation is correct"}], "comment_id": [], "comment_cdate": [], "comment_tcdate": [], "comment_tmdate": [], "comment_readers": [], "comment_writers": [], "comment_reply_content": [], "comment_content": [], "comment_replyto": [], "comment_url": [], "meta_review_cdate": 1551356546188, "meta_review_tcdate": 1551356546188, "meta_review_tmdate": 1551703096349, "meta_review_ddate ": null, "meta_review_title": "Acceptance Decision", "meta_review_metareview": "Overall the reviewers commend the paper for its good clinical motivation and clear writing style. \n\nHowever, all reviewers reject the paper for the following main reasons:\n* Missing methodological novelty since it is a straight-forward application of the pix2pix framework\n* Only marginal improvements over compared techniques with very large standard deviations\n* Qualitative example segmentations which do not seem to reflect the quantitative results with some of the reviewers suggesting they may have been selectively chosen. \n\nFurthermore, the authors did not submit any rebuttals. I thus follow the recommendation of the reviewers to reject the paper. ", "meta_review_readers": ["everyone"], "meta_review_writers": ["MIDL.io/2019/Conference"], "meta_review_reply_count": {"replyCount": 0}, "meta_review_url": ["https://openreview.net/forum?id=HyemZQzExE&noteId=rkx9Kz8SIN"], "decision": "Reject"}