The paper is well-written, with lots of details provided. The studied problem is definitely of great impact and would significantly benefit the community of neuroscience. The reviewer noticed the large size of the problem and appreciates the considerable amount of works achieved, as described by the paper. On the other hand, I also want to point out some associated weakness: 1. To my understanding, two aspects which are the keys to the segmentation performance are: (1) The local DNN evaluation of shape descriptors in terms of energy, and (2) The back-end guidance of (super)voxel agglomeration.

Whenever we train a neural network from scratch, it's weights are initialized with random values. So, if you re-run the same training job again and again, the values used to initialized the weights will keep on changing as they would be randomly generated. Now just imagine, metric of a State of the Art architecture for a given task is 80. You propose a new architecture for the same task and train your model from scratch. After you run it once (assuming all hyper-parameters were just perfect), you get 79.8 metric value.

The room was packed at the annual Machine Learning and the Market for Intelligence conference in Toronto last week. Now in its fifth year, the lengthy name of the event matches the depth of the discussions. But one speaker and her talk stood out to me in particular: Marzyeh Ghassemi, who also happens to be a veteran of Alphabet's Verily, presented "Machine Learning From Our Mistakes." Ghassemi, an assistant professor at the University of Toronto, talked about the importance of predicting actionable insights in health care, the regulation of algorithms, and practice data versus knowledge data. But at the very end, saving the best for last, she emphasized the importance of treating health data as a resource.

Both your and meem1029's arguments ignore the actual history in computer vision. We know exactly how resistant the computer vision community was to learnt feature approaches in the early part of this decade. If, without direct knowledge of the actual submitted document, I had to estimate what is likely to be true when a neural network advocate in 2012 was stating they had a method which covered those 4 bases, and got rejected from CVPR with criticisms like "needs more SIFT" ... well, I would put a very high chance on the ANN person being in the right. There are tons of examples of this in science. Genomics advocates in the early naughties - have you ever heard the debates about "hypothesis free research"?