And just out of curiosity, what did you mean by “the standard shape descriptors are not really the best in my understanding to describe shapes of mitochondria”? Are you referring to morphological binning/clustering?
If you segment profiles of mitochondria (as filled shapes) and make sure individual profiles are not touching each other you can use “standard” object detection and object description features. Depending on a program you will use for that you get various options. For example in MIB (parent of DeepMIB) you can calculate the following features either in pixels or in physical untis (if your images taken with the same magnification, it really helps a lot!)
As you can guess from the list, the most usable metrics are: Area, eccentricity, First/Second axis length and perimeter. These are useful but still quite limited. For example First/Second axis length sounds very nice, but as mitochondria may be bended you will bump into something like shown on the left panel, where both first (longest) and second (shortest) axis lengths are not correct:
The slides on the right, shows a way how I address this problem to calculate the second axes length with the good confidence.
So, my point is that, you can get all kinds of metrics, but they may not be good enough to describe your phenotypes. And because of that I suggested to do measurements manually, this way you skip the step of rechecking the automatic segmentation.
Is there any specific program better suited for this task?
I guess the best one is the one you can handle We do all that kind of stuff in MIB, but I guess you can also do that in Fiji if you wish, but I can’t really suggest anything there as I am not using that myself.
How many pictures more or less do you think it will take for training?
that is quite hard to guess, but the good thing with u-nets, that you can actually get quite good results with relatively small amount of data for training.
But here the notes:
- you will need a computer with modern GPU, otherwise it will be quite slow
- it would be better to beta version of MIB/DeepMIB from here: Release MIB 2.712 BETA · Ajaxels/MIB2 · GitHub as it has way more extensive system for data augmentation
- you have to manually (semi-automatically) segment some mitochondria (as variable as possible) from your images and crop those out as they are not as frequently seen on images (it is possible to do that in MIB in some extent)