gm_wm_exvivo_t2¶
Grey/white matter seg on exvivo human T2w
This segmentation model for T2w human spinal gray and white matter uses a 2D Unet architecture, and was created with the ivadomed package. Training data consisted of 149 2D slices taken from the scan of an ex vivo spinal cord of a single subject, with the gray and white matter from each slice manually segmented for training purposes. The data was provided by the University of Queenland, and through this collaboration, the model was subsequently applied to the development of an ex vivo spinal cord template (https://archive.ismrm.org/2020/1171.html).
Reference¶
Project URL: https://github.com/ivadomed/model_seg_exvivo_gm-wm_t2_unet2d-multichannel-softseg
usage: sct_deepseg gm_wm_exvivo_t2 [-i <file> [<file> ...]] [-o <str>]
[-install]
[-custom-url CUSTOM_URL [CUSTOM_URL ...]]
[-thr <float>] [-largest {0,1}]
[-fill-holes {0,1}]
[-remove-small REMOVE_SMALL [REMOVE_SMALL ...]]
[-qc <folder>] [-qc-dataset <str>]
[-qc-subject <str>] [-qc-plane <str>]
[-qc-seg <file>] [-h] [-v <int>] [-r {0,1}]
INPUT/OUTPUT¶
- -i
Image filename(s) to segment. If segmenting multiple files, separate filenames with a space.
- -o
Output file name. The chosen filename will be used as a base name, and model-specific suffixes will be added to the end depending on the type of output (e.g. ‘_cord.nii.gz’, ‘_gm.nii.gz’, etc.).
TASKS¶
- -install
Install models that are required for specified task.
Default:
False- -custom-url
URL(s) pointing to the
.zipasset for a model release. This option can be used with-installto install a specific version of a model. To use this option, navigate to the ‘Releases’ page of the model, find release you wish to install, and right-click + copy the URL of the.ziplisted under ‘Assets’. Example:sct_deepseg gm_wm_exvivo_t2 -install -custom-url CUSTOM_URLsct_deepseg gm_wm_exvivo_t2 -i t2.nii.gz
PARAMETERS¶
- -thr
Binarize segmentation with specified threshold. Set to 0 for no thresholding (i.e., soft segmentation). Default value is ‘[0.0]’, and was chosen by experimentation (more info at https://github.com/sct-pipeline/deepseg-threshold).
- -largest
Possible choices: 0, 1
Keep the largest connected object from each output segmentation; if not set, all objects are kept.
Default:
0- -fill-holes
Possible choices: 0, 1
If set, small holes in the segmentation will be filled in automatically.
Default:
0- -remove-small
Minimal object size to keep with unit (mm3 or vox). A single value can be provided or one value per prediction class. Single value example: 1mm3, 5vox. Multiple values example: 10 20 10vox (remove objects smaller than 10 voxels for class 1 and 3, and smaller than 20 voxels for class 2).
MISC ARGUMENTS¶
- -qc
The path where the quality control generated content will be saved.
- -qc-dataset
If provided, this string will be mentioned in the QC report as the dataset the process was run on.
- -qc-subject
If provided, this string will be mentioned in the QC report as the subject the process was run on.
- -qc-plane
Possible choices: Axial, Sagittal
Plane of the output QC. If Sagittal, it is highly recommended to provide the
-qc-segoption, as it will ensure the output QC is cropped to a reasonable field of view.Default:
'Axial'- -qc-seg
Segmentation file to use for cropping the QC. This option is useful when you want to QC a region that is different from the output segmentation. For example, it might be useful to provide a dilated cord segmentation to expand the QC field of view.
If
-qc-segis not provided, the default behavior will depend on the value of-qc-plane:‘Axial’: Without ‘-qc-seg’, a sensible crop radius between 15-40 vox will be automatically used, depending on the resolution and segmentation type.
‘Sagittal’: Without ‘-qc-seg’, the full image will be displayed by default. (For very large images, this may cause a crash, so using
-qc-segis highly recommended.)
- -v
Possible choices: 0, 1, 2
Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode.
Default:
1- -r
Possible choices: 0, 1
Remove temporary files.
Default:
1