def METHOD_NAME():
    parser = SCTArgumentParser(
        description='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.'
    )
    mandatory = parser.add_argument_group("\nMANDATORY ARGUMENTS")
    mandatory.add_argument(
        "-i",
        required=True,
        help='Input image. Example: t1.nii.gz',
        metavar=Metavar.file,
    )
    mandatory.add_argument(
        "-c",
        required=True,
        help='Type of image contrast.\n'
             ' t2: T2w scan with isotropic or anisotropic resolution.\n'
             ' t2_ax: T2w scan with axial orientation and thick slices.\n'
             ' t2s: T2*w scan with axial orientation and thick slices.',
        choices=('t2', 't2_ax', 't2s'),
    )
    optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS")
    optional.add_argument(
        "-h",
        "--help",
        action="help",
        help="Show this help message and exit",
    )
    optional.add_argument(
        "-centerline",
        help="Method used for extracting the centerline:\n"
             " svm: Automatic detection using Support Vector Machine algorithm.\n"
             " cnn: Automatic detection using Convolutional Neural Network.\n"
             " viewer: Semi-automatic detection using manual selection of a few points with an interactive viewer "
             "followed by regularization.\n"
             " file: Use an existing centerline (use with flag -file_centerline)",
        required=False,
        choices=('svm', 'cnn', 'viewer', 'file'),
        default="svm")
    optional.add_argument(
        "-file_centerline",
        help='Input centerline file (to use with flag -centerline file). Example: t2_centerline_manual.nii.gz',
        metavar=Metavar.str,
        required=False)
    optional.add_argument(
        "-brain",
        type=int,
        help='Indicate if the input image contains brain sections (to speed up segmentation). This flag is only '
             'effective with "-centerline cnn".',
        required=False,
        choices=(0, 1),
        default=1)
    optional.add_argument(
        "-ofolder",
        help='Output folder. Example: My_Output_Folder',
        required=False,
        action=ActionCreateFolder,
        metavar=Metavar.str,
        default=os.getcwd())
    optional.add_argument(
        "-r",
        type=int,
        help="Remove temporary files.",
        required=False,
        choices=(0, 1),
        default=1)
    optional.add_argument(
        '-v',
        metavar=Metavar.int,
        type=int,
        choices=[0, 1, 2],
        default=1,
        # Values [0, 1, 2] map to logging levels [WARNING, INFO, DEBUG], but are also used as "if verbose == #" in API
        help="Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode")
    return parser