I would like solve some biological problems that would improve the state-of-the-art of biology or bioinformatics. In particular, I want to apply machine learning on light microscopic images. The equipment and experience I have are:

  • Bright-field, dark-field, and phase-contrast microscopy
  • Modern laptop
  • 56-core super-computer with >100 GB of memory (on request)
  • Intricate knowledge of machine learning algorithms and signal processing
  • PhD-level research skills
  • Programming skills that would get me to work at Google
  • Limited knowledge about biology, bioinformatics, and microscopy (yet)

I want to do some publishable research free from all academic hassle. I will do this solely on my own time, without hurry to publish, in an attempt to do something good for the mankind. I can throw a few hundred dollars on the project every two months (or abour 1000 USD per year).

Much of the biological research published in Science, Nature, PNAS, Cell, etc. are so specialized that I find it difficult to detect important problems I could have a good chance of solving given my skill set. Thus, I am asking your help:

  • What kind of software you always wanted for light microscopic research, but did not know how to build?
  • What are some important biological problems you would like to get solved? (For machine learning, problems with a binary decision task are particular well suited -- e.g. "does this person have malaria or not"?)
  • What are some recent, high quality reviews on open problems in biology?
  • Something else?

While my question is a bit broad, I think this goes under the "good intention" (or whatever it is called) SE policy.

  • 1
    $\begingroup$ Neat question, but I'm, afraid there isn't one answer to accept and this one might get voted off... while it's up, though, how about pollen identification? There are ecology, paleoecology, forensics, health, and food safety implications, it's a challenging computer vision problem, and there should be plenty of imagery online to train an expert system with. $\endgroup$ Commented Aug 7, 2014 at 19:08
  • 1
    $\begingroup$ This is a great question and should not be voted closed since it is attempting to address an important issue, which (if solved) can have great implications in automation of imaging and data flow and the type of data obtained! Surely biology stack exchange is designed to help with questions such as this!! Kudos to you @days_of_good. One great place you can start is looking at fiji image-J (image analysis software) and look at its plugins. Most are open-source and it would be great if they could be incorporated in microscope imaging softwares and perform instructive commands to the microscope! $\endgroup$ Commented Aug 7, 2014 at 19:55
  • $\begingroup$ @days_of_good is there a scope for your work to cover fluorescence microscopy such as confocal or snapshot widefield and deconvolution microscopy of z-stacked images? a very very large portion of microscopy in the current scientific landscape uses fluorescence techniques to detect molecules or structures at cellular level, mainly using either fluorescing molecules GFP, RFP, YFP, etc attached to the molecule of interest or antibodies which detect a molecule and they themselves are detected by fluorescing secondary antibodies. The main problem in the field of microscopy is the noise-to-signal. $\endgroup$ Commented Aug 7, 2014 at 20:09
  • $\begingroup$ @Bez No, unfortunately I do not have access to fluorescence microscopy. Is there need for deconvolution methods applied to light microscopic images? I know some blind deconvolution methods, which could maybe used to pass the diffraction limit (kind of super-resolution). $\endgroup$ Commented Aug 8, 2014 at 12:43
  • 1
    $\begingroup$ This is a great and thought provoking question. If it were in chat I would never even have seen it. Many years ago I worked with a scientist at Kodak's research division in Harrow, London. The work was to count the number of photons required for activation of silver halide grains (interpolated from silver nuclei). The bulk of the counting was done on a Cambridge Instruments Image Analyser hooked up to a microscope with a TV camera attached. Not much call for it these days but I could envisage the equipment discussed being used for counting particulates in polluted water and other such tasks. $\endgroup$
    – Ian Lewis
    Commented Apr 22, 2015 at 21:28

4 Answers 4


I know this question is going to close. But, if you want to work something you can work on:

Cryo super-resolution fluorescence imaging


  • CryoFM allows imaging of vitrified biological samples with fluorescence microscopy.
  • There are significant challenges to achieve high-resolution cryoFM imaging.
  • Fluorophore characteristics at low temperature offer additional advantages.
  • Cryo super-resolution fluorescence imaging will give dramatic resolution improvement.

Source: Fluorescence cryo-microscopy: current challenges and prospects.


RE: What kind of software you always wanted for light microscopic research, but did not know how to build?

I research fruit flies and in this field (and many other insect ecology model systems like beetles, moths, butterflies) we use a lot of visually scored data, e.g. body size, wing size, wing morphology, eye colours, bristle numbers, genital morphology, sex comb morphology... the list is huge! One program used is WingMachine - though the link to the software seems to be broken - which can measure morphology aspects of a fly wing.

Something I'd like to be able to do is put a vial of food under a scope and have it quickly count the number of eggs on the surface of the food. I asked a question about it a while back.. This would be very useful, many labs have to count eggs (to make the number of eggs constant in each vial, variation here can have serious effects on the adult fly so control is important in ecology studies) and it is a slow, difficult and highly inaccurate, particularly variable between people. If there was some way of putting the vial under the scope, hitting a button and getting an approximation of the number it would be great!

A colleague counts dead beetles at the moment, I'm sure he'd appreciate a similar kind of program where he could image and have the software count automatically. I think both of these problems would be easy to solve with very similar software. Making software that is easy to "teach" how to recognize individuals is the key.

A slightly more complex bit of machine learning might be getting it to count different phenotypes in one image. Fitness assays in flies often use a wild type fly with dark bodied (ebony) competitors, the body of the wild type is comparatively more yellow. The fitness of the focal wild type fly is then the number of wild type offspring among the total (the dark body phenotype is recessive, therefore when the focal wild type mates with an ebony it produces wild type flies, if two ebonies mate we get a dark body offspring). Here the machine would have to be able to tell the difference and count both.

I'll attach a proper picture from under the scope later, the picture in my previous question was taken with a digital camera, not via a scope but it gives an idea of what it looks like.


You might be interested in reading the article "Machine learning in cell biology – teaching computers to recognize phenotypes" (http://jcs.biologists.org/content/126/24/5529.long)

  • $\begingroup$ Please summarize the relevant aspects of the link in your answer. See the 'provide context for links' section of the Help Center. $\endgroup$
    – J. Musser
    Commented Sep 18, 2014 at 17:14

One of my colleagues does lots of histological work, staining and identifying tissue at the microscopic level. Software that could be helpful to that discipline might be the ability to distinguish among the different types of tissues present, and perhaps calculate the "area" occupied by each tissue type as well as empty space. This would not be unlike a GIS type problem, but I don't know if it fits well with a Yes/No binary decision framework. I don't know if it could be trained to learn to identify specific types of tissue, but perhaps it could recognize each distinct area of a cross section as different from other such areas. Here are a few cross sections to show you what I mean:


enter image description here Source

Smooth muscle:

enter image description here Source

Seminiferous tubules from testes:

enter image description here Source

Notice the different tissue types in each cross section, plus the white space. Each tissue type has different light transmission patterns, which may allow a computer to learn to distinguish among the different tissue types.

  • $\begingroup$ Look into WEKA segmentation algorithms implemented in FIJI/ImageJ. $\endgroup$
    – user560
    Commented Sep 20, 2014 at 14:18

You must log in to answer this question.

Not the answer you're looking for? Browse other questions tagged .