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hi
When using antsCorticalThickness, I found that the segmentation results had regions in the cortical areas that were connected, leading to inaccurate cortical thickness (CT) measurements. Is there any way to resolve this issue?
antsCorticalThickness was developed in the context of human data and it might not generalize cleanly to other species. Given your application, you'll probably need to decompose the pipeline into its components (Atropos + KellyKapowski) and debug each one. But you'd have to figure out the best parameters yourself.
antsCorticalThickness was developed in the context of human data and it might not generalize cleanly to other species. Given your application, you'll probably need to decompose the pipeline into its components (Atropos + KellyKapowski) and debug each one. But you'd have to figure out the best parameters yourself.
The thickness algorithm is somewhat resistant to the "closed sulcus problem", where you have placed your crosshairs, you'll see that thickness is roughly half of the distance across the whole joined area. Of course, the more you can resolve the correct tissue labels, the better it will be.
I agree though that you will probably need to customize the scripts to do what you need.
Operating system and version
Ubuntu 20.04
CPU architecture
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ANTs code version
ants-2.5.0
ANTs installation type
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Summary of the problem
hi
When using antsCorticalThickness, I found that the segmentation results had regions in the cortical areas that were connected, leading to inaccurate cortical thickness (CT) measurements. Is there any way to resolve this issue?
My species is a dog.
thanks
68_BrainSegmentationPosteriors2.nii.gz
68_BrainSegmentationPosteriors3.nii.gz
68_CorticalThickness.nii.gz
Commands to reproduce the problem.
antsCorticalThickness.sh -d 3 -a ${input} -e ${sub_template} -m ${sub_template_mask} -p ${prior_dir}/template_%d.nii.gz -y 0 -w 0.25 -o ${ct_output_dir}/${output_name}_
Output of the command with verbose output.
.
Data to reproduce the problem
.
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