sct_deepseg_lesion¶
MS lesion Segmentation using convolutional networks. Reference: Gros C et al. Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks. Neuroimage. 2018 Oct 6;184:901-915.
usage: sct_deepseg_lesion -i <file> -c {t2,t2_ax,t2s} [-h]
[-centerline {svm,cnn,viewer,file}]
[-file_centerline <str>] [-brain {0,1}]
[-ofolder <str>] [-r {0,1}] [-v <int>]
MANDATORY ARGUMENTS¶
- -i
Input image. Example: t2.nii.gz
- -c
Possible choices: t2, t2_ax, t2s
Type of image contrast.
t2
: T2w scan with isotropic or anisotropic resolution.t2_ax
: T2w scan with axial orientation and thick slices.t2s
: T2*w scan with axial orientation and thick slices.
OPTIONAL ARGUMENTS¶
- -centerline
Possible choices: svm, cnn, viewer, file
Method used for extracting the centerline:
svm
: Automatic detection using Support Vector Machine algorithm.cnn
: Automatic detection using Convolutional Neural Network.viewer
: Semi-automatic detection using manual selection of a few points with an interactive viewer followed by regularization.file
: Use an existing centerline (use with flag-file_centerline
)
Default: “svm”
- -file_centerline
Input centerline file (to use with flag
-centerline
file). Example:t2_centerline_manual.nii.gz
- -brain
Possible choices: 0, 1
Indicate if the input image contains brain sections (to speed up segmentation). This flag is only effective with
-centerline cnn
.Default: 1
- -ofolder
Output folder. Example: My_Output_Folder
Default: “/home/docs/checkouts/readthedocs.org/user_builds/spinalcordtoolbox/checkouts/stable/documentation/source”
- -r
Possible choices: 0, 1
Remove temporary files.
Default: 1
- -v
Possible choices: 0, 1, 2
Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode
Default: 1