I work with high-resolution micro-CT scans of fossils, obtained with Skyscan 1272 and 1273 micro-CTs. Since the data comes as stacks of .png images, I use the Slice Morph extension to load them. Because of the condition of the fossils and the large number of elements I need to segment for later reconstruction in other software, I mainly use the “Grow from seeds” tool. It gives very good results and saves me a lot of time.
Performance has become extremely slow, even though I upgraded my setup. Normally, the scans I work with range from about 200 MB up to ~3.5 GB. At first, the initial segments only took about 2 minutes, but now the last one I did took more than 2 hours just for “Grow from seeds” to finish.
During those hours, I noticed that RAM usage went up to a maximum of ~55 GB. I also monitored CPU activity with Process Lasso and saw that cores 0, 9, and 11 had the highest load (around 20% each on average), while the others (1–8, 10, 12–15) stayed mostly at 0–1%. At one point, core 0 reached ~45%, while cores 9 and 11 peaked at 55% and 63%, respectively.
Dataset information
I opened the dataset by loading the .mrml scene in Slicer. The main volume is stored as an .nrrd file, with a size of 2.7 GB. Below are the relevant details:
File type: NRRD
Dimensions: 1972 × 1084 × 1313
Voxel spacing: 0.011 mm (isotropic, 11 μm per voxel)
Voxel type: unsigned char (1 component)
Volume bounds: X: 0–1971, Y: 0–1083, Z: 0–1312
Resolution: ~11 μm per voxel
Total size (on disk): 2.7 GB
Hardware setup
CPU: Ryzen 9 9950X3D (Zen 5)
RAM: 64 GB DDR5 @ 6000 MHz (EXPO enabled in BIOS)
Storage: NVMe SSD
OS: Windows 11 Pro
All cores, threads, and SMT are enabled.
What I’ve tried
Cropping: not possible, since there is no margin left in the region of interest.
Resampling to lower resolution: not acceptable, since I lose the little definition and contrast between bone structures.
Import with “half resolution”: sometimes reduces size, but too much detail is lost.
Alternative segmentation tools in Slicer: none of them have worked well for my case.
Is there any way to significantly improve the processing time of “Grow from seeds” for datasets of this size, or could I be doing something wrong in my workflow?
I sincerely appreciate your time and your responses.
I don’t think you’re doing anything wrong in your workflow. Please note that your image is around 50-80 times larger than a normal CT (in terms of number of voxels). Also, from your description it seems to me that you are using most of the extent of the image for grow from seeds, which usually also does not occur with normal medical images (we segment a smaller part). So putting these two factors together, the two hours unfortunately seems understandable to me.
We have started moving some Segment editor effects on the GPU several years ago, but that line died away, because the direct motivation ceased to exist (the work with fractional labelmaps as I remember). If there is funding, I’m sure it is possible to improve Segment editor effects (even one by one) to use the GPU.
I think others will chime in with their points of view as well.
I think the best approach will likely be to crop your image volume into several smaller pieces and run grow from seeds on each one, then merge them back together. This may cause issues near block boundaries, but I think this is probably still your best bet, and you may be able to alleviate boundary issues by re-running grow from seeds near these regions (I can imagine a couple possibilities of how this could work, but am not sure which would work best in practice). Grow from seeds is fundamentally a local operation, so cropping your image into blocks and running a quick grow from seeds on each block should basically work. If your seed regions are so sparse that blocks might be missing a seed that they should have, then your even the full image grow from seed result probably won’t work very well.
You are not doing anything wrong. These are your main options if you want to get results fasts. You can do the segmentation in half resolution version of your data, get your grow from the seed results fast (with the understanding that it may miss small detail). To fix the issues then you can resample your segmentaiton to the original resolution, and import the full resolution imagestack and fine tune your segmentation.
Alternatively, as suggested, split your full resolution data in 4 smaller volumes via CropVolume (make sure each subvolume overlaps a bit, maybe 40-50 voxels), and then run grow from the seeds independently and merge the segments label.
Thank you all for your responses. I have a question about that methodology. For example, the dataset I am currently working on is a fragment of a dentary with 4 molars. Could I then crop those 4 teeth, segment them using GFS in the sub-volume, and then copy the finished segment to the segmentation of the main volume
How can I resample the segmentation to the original resolution, just importing the imagestack?
(ie., if you have done the segmentation in half-resolution volume, and transfer it to the full resolution version you need to oversample the existing segmentation by 2)