Paper Review: Distributed 3D image segmentation using micro-labor workforce

DP2: Distributed 3D image segmentation using micro-labor workforce Richard J. Giuly, Keun-Young Kim and Mark H. Ellisman. Bioinformatics doi: 10.1093/bioinformatics/btt154

I just love this paper. It is just at that intersection of quirky and serious which makes you laugh while being dead serious (I admit that it only makes you laugh if you have a very particular sense of humour).

The quirky aspect is the following: they authors solve complex three-dimensional image segmentation problems by using a Amazon Mechanical Turk crowd of untrained workers to do it!

They do so by reducing the problem to a serious of simple yes/no questions that can be understood by people without any background in neurology.

The serious aspect is that it seems that it actually works. It gives good segmentations without resorting to highly-paid experts or very fancy algorithms.


One of the main results that has come out of bioimage informatics that surprises computer vision people and biologists is the following:

Computers can be better than people at bioimage informatics

We (humans) are excellent at face recognition (a task we evolved to do and grew up doing), which is why computer vision researchers who work on this sort of problem tend to revere the human visual systems. However, we just cannot recognize the endoplasmic reticulum. Even trained cell biologists are really not that good at recognising the ER in fluorescent microscopy image.

We can perhaps read this paper in the context in the context of the general discussion of human/computer partnerships. What can humans do for the computer and vice-versa?


I have now gone off on a tangent, but the paper does present a fairly typical image processing pipeline:

  1. Add Gaussian blur to images
  2. Over-segment into super pixels
  3. Merge superpixels into segmentations by performing repeated queries of the form:

Q: Should region A and region B be merged together?

This is all very standard except that Q is performed by humans. In fact, what I think is the main contribution of this paper: Q is performed by non-experts. And it works. By dumbing it down for the human, the computer actually ends up doing well.

It’s briliant!


The thing I do wonder is why this was an Application paper instead of a Research paper. It presents what I think is an interesting new perspective, which seems more valuable than the software (which, by the way, is not even open-source; which limits its worth as well). This also meant that the authors only had two pages in which to expose their methods.

I would have loved to read more results and discussion. I half-suspect that this was not the authors’ choice and can only hope that the increasing digitalization of research publications removes these page limitations.


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