for the provided details that are extremely helpful for those who may be able to help.
I can read the original z?-stacks and I understand that you are mainly interested in a single slice of every stack, e.g. slice #4 or #5 of stack “OPA1 KO.lsm”.
For a reasonable classification as indicated in image “sample opa1 ko.png” I see little chance no matter what method you use.
Please note that in your case, segmentation is not the proper term because segmentation doesn’t care so much about the semantics. In the first place segmentation would separate all dot-like structures but would hardly differentiate between mitochondria and non-mitochondria.
For me, as an untrained observer in this field, it is even impossible to distinguish both.
In short, a machine needs to know what you know but it only understands mathematics. Consequently, we need a formal description of what distinguishes mitochondria and non-mitochondria. And for this purpose I fear that the spatial resolution of the images is insufficient, but I may be wrong.
If you have formal definitions of separating features and if these features can be extracted from the images, you could train a classifier. If pixel based classification of TWS is suited can be doubted.
Regarding histograms and bit-depth:
If you have an image and look at the histogram, you will see how many pixels have a certain gray level. If the image is 8bit, it can have 2^8 = 256 gray levels. The histogram tells you, if you really use this range or not. If an image has more than a few pixels of value 255, it must be regarded as over-exposed. If however, you have no pixels with values, say above 180, then you don’t really use the range which means you loose information.
If you have a camera that is able to grab images with more than 256 gray levels, then please do so. Such images will be represented as 16bit images (they can show up to 2^16 = 65536 gray levels) even if they actually show e.g. only 1000 gray levels (10bit). This has to do with computer preferences …
I hope you got the points up to now.
In any case please study the ImageJ User Guide: