Paper Review: Automated prior knowledge-based quantification of neuronal patterns in the spinal cord of zebrafish

Automated prior knowledge-based quantification of neuronal patterns in the spinal cord of zebrafish by Johannes Stegmaier, Maryam Shahid, Masanari Takamiya, Lixin Yang, Sepand Rastegar, Markus Reischl, Uwe Strähle, and Ralf Mikut. in Bioinformatics (2013) [DOI]

It’s been a while since I’ve had a paper review, even though one of my goals is to give more space to bioimage informatics. So, I will try to make up for it in the next few weeks. This is a paper which is not exactly hot off the press (it came out two months ago), but still very recent.

The authors are working with zebrafish. Unfortunately, I am unable to evaluate the biological results as I do now know much about zebrafish, but I can appreciate the methodological contributions. I will illustrate some of the methods based on a Figure (Fig 2) from the paper:

Figure 2

The top panel is the data (a fish spinal coord, cropped out of a larger field), the next two a binarization of the same data and a line fit (in red). Finally, the bottom panel shows the effect of straightening the image to a line. This allows for comparison between different images by morphing them all to a common template. The alignment is performed on only one of the channels, while the others can carry complementary information.


This is very similar to work that has been done in straightening C. elegans images (e.g., Peng et al., 2008) in both intent and some of the general methods (although there you often morph the whole space instead of just a band of interest). It is a bit unfortunate that the bioimage informatics literature sometimes aggregates by model system when many methods can profitably be used across problems.


Finally, I really like this visualization, but I need to give you a bit of background to explain it (if I understood it correctly). Once a profile has been straightened (panel D in the figure above), you can summarize it by averaging along the horizontal dimension to get the average intensity at each location (where zero is the centre of the spinal coord) [1]. You can then stack these profiles (analogously to what you’d do to obtain a kinograph) as a function of your perturbation (in this case, a drug concentration):

Figure 6

This is Figure 6 in the paper.

The effect of the drug (and saturation) become obvious.


As a final note, I’ll leave you with this quote from the paper, which validates some of what I said before: the quality of human evaluation is consistently over-estimated:

Initial tests unveiled intra-expert and inter-expert variations of the extracted values, leading to the conclusion that even a trained evaluator is not able to satisfactorily reproduce results.

[1] The authors average a different marker than the one used for straightening, but since I know little about zebrafish biology, I focus on the methods.

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