gm_sc_7t_t2star

SC/GM seg on T2*-weighted contrast at 7T

This multiclass model (SC/GM) was developed by N.J. Laines Medina, V. Callot and A. Le Troter at the Center for Magnetic Resonance in Biology and Medicine (CRMBM-CEMEREM, UMR 7339, CNRS, Aix-Marseille University, France). Training data consisted of T2*w scans acquired at 7T from 72 subjects: 34 healthy controls, 25 patients with ALS, 13 patients with MS. The model was validated by comparing with single-class models using 9-fold Cross-Validation. It was enriched by integrating a hybrid data augmentation (composed of classical geometric transformations, MRI artifacts, and real GM/WM contrasts distorted with anatomically constrained deformation fields). Finally, it was tested with an external multicentric database. For more information, see the following URL.

Reference

@misc{medina20212d,
      title={2D Multi-Class Model for Gray and White Matter Segmentation of the Cervical Spinal Cord at 7T},
      author={Nilser J. Laines Medina and Charley Gros and Julien Cohen-Adad and Virginie Callot and Arnaud Le Troter},
      year={2021},
      eprint={2110.06516},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

Project URL: https://github.com/ivadomed/model_seg_gm-wm_t2star_7t_unet3d-multiclass

usage: sct_deepseg gm_sc_7t_t2star [-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 .zip asset for a model release. This option can be used with -install to 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 .zip listed under ‘Assets’. Example: sct_deepseg gm_sc_7t_t2star -install -custom-url CUSTOM_URL sct_deepseg gm_sc_7t_t2star -i t2.nii.gz

PARAMETERS

-thr

Binarize segmentation with specified threshold. Set to 0 for no thresholding (i.e., soft segmentation). Default value is ‘[0.5]’, 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-seg option, 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-seg is 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-seg is 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