use any atlas

brainreg/backend/niftyreg/parser.py

@@ -0,0 +1,120 @@
+from imlib.general.numerical import check_positive_int, check_positive_float
+
+
+def niftyreg_parse(parser):
+    niftyreg_opt_parser = parser.add_argument_group(
+        "NiftyReg registration backend options"
+    )
+    niftyreg_opt_parser.add_argument(
+        "--affine-n-steps",
+        dest="affine_n_steps",
+        type=check_positive_int,
+        default=6,
+        help="Registration starts with further downsampled versions of the "
+        "original data to optimize the global fit of the result and "
+        "prevent 'getting stuck' in local minima of the similarity "
+        "function. This parameter determines how many downsampling "
+        "steps are being performed, with each step halving the data "
+        "size along each dimension.",
+    )
+    niftyreg_opt_parser.add_argument(
+        "--affine-use-n-steps",
+        dest="affine_use_n_steps",
+        type=check_positive_int,
+        default=5,
+        help=" Determines how many of the downsampling steps defined by "
+        "-affine-n-steps will have their registration computed. "
+        "The combination --affine-n-steps 3 --affine-use-n-steps 2 "
+        "will e.g. calculate 3 downsampled steps, each of which is "
+        "half the size of the previous one but only perform the "
+        "registration on the 2 smallest resampling steps, skipping the "
+        "full resolution data. Can be used to save time if running the "
+        "full resolution doesn't result in noticeable improvements.",
+    )
+    niftyreg_opt_parser.add_argument(
+        "--freeform-n-steps",
+        dest="freeform_n_steps",
+        type=check_positive_int,
+        default=6,
+        help=" Registration starts with further downsampled versions of the "
+        "original data to optimize the global fit of the result and "
+        "prevent 'getting stuck' in local minima of the similarity "
+        "function. This parameter determines how many downsampling "
+        "steps are being performed, with each step halving the data "
+        "size along each dimension.",
+    )
+    niftyreg_opt_parser.add_argument(
+        "--freeform-use-n-steps",
+        dest="freeform_use_n_steps",
+        type=check_positive_int,
+        default=4,
+        help="Determines how many of the downsampling steps defined by "
+        "--freeform-n-steps will have their registration computed. "
+        "The combination --freeform-n-steps 3 --freeform-use-n-steps "
+        "2 will e.g. calculate 3 downsampled steps, each of which is "
+        "half the size of the previous one but only perform the "
+        "registration on the 2 smallest resampling steps, skipping the "
+        "full resolution data. Can be used to save time if running the "
+        "full resolution doesn't result in noticeable improvements.",
+    )
+    niftyreg_opt_parser.add_argument(
+        "--bending-energy-weight",
+        dest="bending_energy_weight",
+        type=check_positive_float,
+        default=0.95,
+        help="Sets the bending energy, which is the coefficient of the "
+        "penalty term, preventing the freeform registration from "
+        "over-fitting. The range is between 0 and 1 (exclusive) "
+        "with higher values leading to more restriction of the "
+        "registration.",
+    )
+    niftyreg_opt_parser.add_argument(
+        "--grid-spacing",
+        dest="grid_spacing",
+        type=int,
+        default=-10,
+        help="Sets the control point grid spacing in x, y & z. Positive "
+        "values are interpreted as real values in mm, negative values "
+        "are interpreted as the (positive) distances in voxels. Smaller "
+        "grid spacing allows for more local deformations but increases "
+        "the risk of over-fitting.",
+    )
+    niftyreg_opt_parser.add_argument(
+        "--smoothing-sigma-reference",
+        dest="smoothing_sigma_reference",
+        type=float,
+        default=-1.0,
+        help="Adds a Gaussian smoothing to the reference (the one being "
+        "registered to) image, with the sigma defined by the number. "
+        "Positive values are interpreted as real values in mm, "
+        "negative values are interpreted as distance in voxels.",
+    )
+    niftyreg_opt_parser.add_argument(
+        "--smoothing-sigma-floating",
+        dest="smoothing_sigma_floating",
+        type=float,
+        default=-1.0,
+        help="Adds a Gaussian smoothing to the floating image (the one being "
+        "registered), with the sigma defined by the number. Positive "
+        "values are interpreted as real values in mm, negative values "
+        "are interpreted as distance in voxels.",
+    )
+    niftyreg_opt_parser.add_argument(
+        "--histogram-n-bins-floating",
+        dest="histogram_n_bins_floating",
+        type=check_positive_int,
+        default=128,
+        help="Number of bins used for the generation of the histograms used "
+        "for the calculation of Normalized Mutual Information on "
+        "the floating image.",
+    )
+    niftyreg_opt_parser.add_argument(
+        "--histogram-n-bins-reference",
+        dest="histogram_n_bins_reference",
+        type=check_positive_int,
+        default=128,
+        help="Number of bins used for the generation of the histograms used "
+        "for the calculation of Normalized Mutual Information on the "
+        "reference image",
+    )
+    return parser