tumor_edema_cavity_t1_t2

Multiclass cord tumor/edema/cavity segmentation

This segmentation model for T1w and T2w spinal tumor, edema, and cavity segmentation uses a 3D UNet architecture, and was created with the ivadomed package. Training data consisted of a subset of the dataset used for the model tumor_t2, with 243 subjects in total: 49 with tumors of type Astrocytoma, 83 with Ependymoma, and 111 with Hemangioblastoma. For each subject, the requisite parts of the affected region (tumor, edema, cavity) were segmented individually for training purposes. This model is used in tandem with another model for specialized cord localisation of spinal cords with tumors (https://github.com/ivadomed/findcord_tumor).

Reference

@article{LEMAY2021102766,
         title={Automatic multiclass intramedullary spinal cord tumor segmentation on MRI with deep learning},
         journal={NeuroImage: Clinical},
         volume={31},
         pages={102766},
         year-2021},
         issn-2213-1582},
         doi-https://doi.org/10.1016/j.nicl.2021.102766},
         url-https://www.sciencedirect.com/science/article/pii/S2213158221002102},
         author-Andreanne Lemay and Charley Gros and Zhizheng Zhuo and Jie Zhang and Yunyun Duan and Julien Cohen-Adad and Yaou Liu},
         keywords-Deep learning, Automatic segmentation, Spinal cord tumor, MRI, Multiclass, CNN}
}

Project URL: https://github.com/ivadomed/model_seg_sctumor-edema-cavity_t2-t1_unet3d-multichannel

usage: sct_deepseg tumor_edema_cavity_t1_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}]
                                            [-c <str> [<str> ...]]

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.).

-c

Possible choices: t1, t2, t2star

Contrast of the input. Specifies the contrast order of input images (e.g. -c t1 t2)

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 tumor_edema_cavity_t1_t2 -install -custom-url CUSTOM_URL sct_deepseg tumor_edema_cavity_t1_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 ‘[0.5, 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