gm_wm_mouse_t1

Exvivo mouse GM/WM segmentation for T1w contrast

This segmentation model for gray and white matter segmentation of exvivo mice spinal cords uses an NNunet architecture, and was created with the nnUNetV2 package. It is a multiclass model, outputting segmentations for both the grey matter and white matter.Training data consisted of 22 mice with different numbers of chunks, for a total of 72 MRI 3D images. Each training image was T2-weighted, had a size of 200x200x500, and had a resolution of 0.05mm isotropic. Training data was provided by the Balgrist Center atthe University of Zurich.

Reference

@software{cohen_adad_2024_10819207,
          author={Cohen-Adad, Julien},
          title={{Segmentation model of ex vivo mouse spinal cord white and gray matter}},
          month=mar,
          year=2024,
          publisher={Zenodo},
          version={v0.5},
          doi={10.5281/zenodo.10819207},
          url={https://doi.org/10.5281/zenodo.10819207}
}

Project URL: https://github.com/ivadomed/model_seg_mouse-sc_wm-gm_t1

usage: sct_deepseg gm_wm_mouse_t1 [-i <file> [<file> ...]] [-o <str>] [-install]
                                  [-custom-url CUSTOM_URL [CUSTOM_URL ...]]
                                  [-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}]
                                  [-test-time-aug]

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_wm_mouse_t1 -install -custom-url CUSTOM_URL sct_deepseg gm_wm_mouse_t1 -i t2.nii.gz

PARAMETERS

-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).

-test-time-aug

Perform test-time augmentation (TTA) by flipping the input image along all axes and averaging the resulting predictions. Note: The time it takes to run the model will increase due to the additional predictions.

Default: False

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