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Implementation of CODEXProcessing on GPU #1
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…et for best focus
Hi @eric-czech, @ashstatic, Eric, truly amazing job! I think the easiest way to move forward with it would be if you could pack it into a Python script with the following parameters:
We are planning on calling this Python script from a command line, the parent process being CODEXServer, a small and simple image processing/storage server that we are writing together with @vishal266 that will be accepting the raw images over the network, processing them, segmenting/quantifying the results, storing on an internal RAID | local NAS | Cloud and serving them to the CODEXViewer FIJI plugin here's the repo Does that sound reasonable? Let me know your thoughts! Nikolay |
@eric-czech, as for the best focusing, it looks amazing, the only thing that I am curious about is whether it could get thrown off by some bright but irrelevant objects (like pieces of dust or tissue debris) in the image field of view. also, could we talk at some point about the image segmentation? I have been trying to impove the segmentation accuracy using deep leaning and TF. For now I have implemented some basic feature enhancement using a ResNet model in tflearn/tensorflow, but I am not sure how to proceed with some downstream steps, i.e. what's the most efficient way to turn this into cell objects also and how to use TF gradient descent optimization to figure out the single-cell intensities - perhaps you could help us think about it? |
Thanks @nsamusik ! Yep, I'll work on some CLI with those arguments (which all sound reasonable). Do you have datasets in S3 or elsewhere from ImageFormatter with the new naming convention that can be shared in a public project like this? No worries if not -- I'll just keep using simulated experiment data. re Focus Classifier: It works by averaging predictions from 84 pixel patches so I haven't tested it enough to say how fragile it is in a single patch but regardless, as long as there aren't anomalies in every patch then it certainly offers at least some resilience to that. re Segmentation: Nice! What data did you use to train the resnet? Or was it something pre-trained on a similar problem? I definitely have some thoughts on what I've seen for getting instance/object segmentation based on pixel classification though so should we schedule a call for next week sometime? I had a question about CODEXServer too. |
Hi Eric,
Here's the example formatter output, right now it's slighly out of sync
with what I said, but I think we will eventually change it to just
T###_C###_Z###, where the numbers will represent timepoint(cycle), channel,
z-plane.
The point is that if you open it in ImageJ in as an image sequence in the
alphanumeric order, you can then easily convert it into a hyperstack in the
default CZT order.
https://drive.google.com/file/d/1B-ggmrPKKFfjL2SyFMev8yt7JgyM_2cy/view?usp=sharing
My next week is free! When would you like to talk?
Nikolay
On Fri, Apr 20, 2018 at 12:21 PM Eric Czech ***@***.***> wrote:
Thanks @nsamusik <https://github.com/nsamusik> !
Yep, I'll work on some CLI with those arguments (which all sound
reasonable). Do you have datasets in S3 or elsewhere from ImageFormatter
with the new naming convention that can be shared in a public project like
this? No worries if not -- I'll just keep using simulated experiment data.
*re Focus Classifier:* It works by averaging predictions from 84 pixel
patches so I haven't tested it enough to say how fragile it is in a single
patch but regardless, as long as there aren't anomalies in every patch then
it certainly offers at least some resilience to that.
*re Segmentation:*
Nice! What data did you use to train the resnet? Or was it something
pre-trained on a similar problem?
I definitely have some thoughts on what I've seen for getting
instance/object segmentation based on pixel classification though so should
we schedule a call for next week sometime? I had a question about
CODEXServer too.
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Hi @nsamusik (cc: @ashstatic),
I was working on getting the CODEXProcessor part of the pipeline onto GPUs (or at least most of it) and thought I'd share some results of that:
Apparently, it can also be used to report progress too which could be nice in a distributed cluster.
Please let me know if you have any thoughts or suggestions, otherwise I'll try to wrap this up and document a script to run the whole CODEXProcessor component like before with the deconvolution script.
Thanks!