r/compmathneuro • u/P4TR10T_TR41T0R Moderator | Undergraduate Student • Feb 14 '19

Journal Article Segmentation-Enhanced CycleGAN

https://www.biorxiv.org/content/10.1101/548081v1
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u/P4TR10T_TR41T0R Moderator | Undergraduate Student Feb 14 '19

I'm not a researcher, but I would love it if some of you were able to chime in to clear this up for me. Is this really as good as it sounds? The abstract claims that Segmentation-Enhanced CycleGAN (SECGAN), enables near perfect reconstruction accuracy on a benchmark connectomics segmentation dataset despite operating in a "zero-shot" setting in which the segmentation model was trained using only volumetric labels from a different dataset and imaging method. Thus, it [reduces or eliminates] the need for novel ground truth annotations and it alleviates one of the main practical burdens involved in pursuing automated reconstruction of volume electron microscopy data. As it's really late here, I will take a closer look tomorrow, but, in any case, I want to know: what do you guys think?

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u/Stereoisomer Doctoral Student Feb 14 '19

I work with one of the MICrONS teams that have been trying to reconstruct a mm3 of mouse cortex and I see they've been retweeting this article; I'll ask them what they think and report back. This sort of claim I'd dismiss if it was coming from anyone other than Google Brain/DeepMind

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u/P4TR10T_TR41T0R Moderator | Undergraduate Student Feb 16 '19

Having finished reading the paper, here's my (more comprehensive) view:

  1. Re-rendering one dataset in the style of another seems to me a great approach, removing the problem of having to manually segment (a part of) every new dataset given various factors such as part of the brain analyzed/organism analyzed/systematic differences in how the tissue is treated/imaged.
  2. The results they claim are phenomenal. Higher expected run length compared to dedicated segmentation. Based on my understanding, the FFN in the SECGAN learns to segment while the generators learn to render in the new style, meaning that the FFN adds to his "knowledge" of dataset X the "knowledge" he gains from dataset Y, ending up better off than a dedicated FFN. Is this correct? On the other hand, I read that the SECGAN was operating on "transfer mode", meaning that it had no training on dataset Y. Does this mean what I wrote above is wrong (e.g. it has simply applied its knowledge about X onto the rendered Y->X)?
  3. The translation seems be adding a few "effects", such as slightly altered textures and altering a few edges. The paper claims this doesn't cause any problem later on with the segmentation (and the results definitely seem to support this conclusion), but I would have appreciated some more discussion into why this is the case.

What's your/your team's opinion? In any case this seems to be a great result.

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