There are few things going on.
First, I can replicate the extreme lag when placing the landmarks on 3D models, and that’s directly related to the size of the model. My suggestions of disabling the interactions and other things didn’t help much, so I would like to hear what @lassoan suggest to improve that. So hold on that one.
There are other things you should do that will make your life easier. I noticed that the tiff stack contains very tight intensity ranges (min = -0.0086, max =0.0765), and the data is represented as a float32 data type. In my experience this is very uncommon for microCT datasets. Most of the time mCT are either 8 bit (if it’s a dry skull) with 256 discrete intensities or, 16 bit (usually soft tissue, or contrast enhanced) with 65K discrete values. Float uses much more memory than either of these. So here the steps you can do rescale and cast your image with almost no loss in detail.
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Import your data with ImageStacks as usual.
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under the Module finder search for
SimpleFilters -
Filters search option type **rescale **
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Enter 0 as output minimum and 255 as output maximum, and create new output volume and hit apply
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After this is completed type Cast
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As the input volume choose the output volume you specified in the previous step.
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Set the Output pixel type to uint8_t (unsigned 8 bit, since we choose 0-255 range in the previous step).
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Set the output volume same as the input volume and hit apply
After these operations you will have a new 8 bit volume. Air background would be around 0, soft tissue surrounding the skull will be 60-90 range, and the bone will be about 100+ values (I just poked around, these may not be entirely true). After this operations notice that how fast the volume rendering will become. You will have to create new set of volume rendering properties for this volume. If you find that the 255 intensity range is not sufficient, you can go back to the rescaling step and enter the range 0-65535 and then in the case choose uint16_t (unsigned 16 bit). Make sure to save the final volume as NRRD file so that you don’t have to repeat these steps.
These will not impact the slowness you encounter during the landmarking of the 3D model from segmentation. Unless @lassoan have other suggestions, if you have feel like you need to the landmarking on the model, you will need to use a slightly lower resolution for faster picking.
(3D model on the left, volume rendering on the right dervied from this 8 bit data)
I will comment on the ImageStacks and ROI operations in the next one.