I decided to blog my old papers (from when I did not have a science blog), mostly because of Melissa Terra’s blog (although I cannot hope to have as much success as she had). In any case, expect the next few weeks to go back to the past.
I will start with this one:
NUCLEAR SEGMENTATION IN MICROSCOPE CELL IMAGES: A HAND-SEGMENTED DATASET AND COMPARISON OF ALGORITHMS by Luis Pedro Coelho, Aabid Shariff, and Robert F. Murphy in Biomedical Imaging: From Nano to Macro, 2009. ISBI ’09. IEEE International Symposium on, 2009. DOI: 10.1109/ISBI.2009.5193098 [Pubmed Central open access version]
It’s more of a solid paper than a one announcing a major breakthrough, so it is interesting that this is currently my most cited paper (according to Google Scholar).
The original question of this paper was very simple: is it worth it to code up and run a complex segmentation algorithm over a simple one on that we were working with?
I hand-segmented a bunch of images from our datasets. Frankly, if I knew how much work this would take; I’d not have done it. And I would not have written this paper. I believe that this is why it became widely cited: a lot of people understand the value of the dataset (and use it for their work).
At the centre of the paper, we presented images such as this one, which had been manually segmented (by me and a subset by Aabid Shariff, according to the label it twice principle):
We then implemented some automatic segmentation algorithms and measured which were best able to reproduce the human labeled data.
1. The method which won was by Lin et al., which is a model-based method . In the meanwhile, however, other groups have reported better results on our dataset (list of citations at Google Scholar).
This means that it is worth it to run a more complex method.
These indices do not take the pixel location into account. We propose a new metric that does, what we call a spatially-aware evaluation method, the normalised sum of distances (NSD), which does.
3. The NSD metric does better than Rand or jaccard .
Another interesting result is that the mean pixel value is a very good threshold for fluorescent microscopy.
Here is the reproducible research archive for this paper.
|||Yes, their model is in 3D, while our data was 2D. I just don’t want to get into that game of making a minor and obvious tweak to an existing algorithm and calling it new. We used their method with the obvious adaptations for our data.|
|||Nowadays, I might try to develop a metric based on random walks as well. The NSD has the advantage that it is very fast to compute.|