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ntustison merged 2 commits into from
May 13, 2026
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ENH: Port NBM and CIT186 functionality to ANTsPyNet. #221

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92b6097
ENH: Port NBM and CIT186 functionality to ANTsPyNet.
ntustison May 13, 2026
f34fd62
BUG: braine xtraction.
ntustison May 13, 2026
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2 changes: 2 additions & 0 deletions antspynet/utilities/__init__.py
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Expand Up @@ -89,6 +89,8 @@
from .desikan_killiany_tourville_labeling import desikan_killiany_tourville_labeling
from .harvard_oxford_atlas_labeling import harvard_oxford_atlas_labeling
from .cerebellum_morphology import cerebellum_morphology
from .nbm_labeling import nbm_labeling
from .cit168_labeling import cit168_labeling
from .brain_age import brain_age
from .mri_super_resolution import mri_super_resolution
from .quality_assessment import tid_neural_image_assessment
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215 changes: 215 additions & 0 deletions antspynet/utilities/cit168_labeling.py
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@@ -0,0 +1,215 @@
import numpy as np
import ants

import keras
from keras import layers, ops, Model

def cit168_labeling(t1, verbose=False):

"""

Perform CIT168 segmentation in T1 images using Pauli atlas (CIT168)
labels described in https://pubmed.ncbi.nlm.nih.gov/29664465/

group_labels = [0,7,8,9,23,24,25,33,34]

group_labels = [0,1,2,5,6,17,18,21,22]

The labeling is as follows:

Label 1: BN_STR_Pu_Left
Labeļ 2: BN_STR_Ca_Left
Label 3: BN_STR_NAC_Left. (not modeled)
Label 4: EXA_Left (not modeled)
Label 5: BN_GP_GPe_Left
Label 6: BN_GP_GPi_Left
Label 7: MTg_SN_SNc_Left
Label 8: MTg_RN_Left
Label 9: MTg_SN_SNc_Left
Label 10: MTg_VTR_PBP_Left (not modeled)
Label 11: MTg_VTR_VTA_Left (not modeled)
Label 12: BN_GP_VeP_Left (not modeled)
Label 13: THM_ETH_HN_Left (not modeled)
Label 14: Die_HTH_Left (not modeled)
Label 15: Die_HTH_MN_Left (not modeled)
Label 16: Die_STH_Left (not modeled)
Label 17: BN_STR_Pu_Right
Label 18: BN_STR_Ca_Right
Label 19: BN_STR_NAC_Right (not modeled)
Label 20: EXA_Right (not modeled)
Label 21: BN_GP_GPe_Right
Label 22: BN_GP_GPi_Right
Label 23: MTg_SN_SNc_Right
Label 24: MTg_RN_Right
Label 25: MTg_SN_SNr_Right
Label 26: MTg_VTR_PBP_Right (not modeled)
Label 27: MTg_VTR_VTA_Right (not modeled)
Label 28: BN_GP_VeP_Right (not modeled)
Label 29: THM_ETH_HN_Right (not modeled)
Label 30: Die_HTH_Right (not modeled)
Label 31: Die_HTH_MN_Right (not modeled)
Label 32: Die_STH_Right (not modeled)
Label 33: ReferenceRegion_Left
Label 34: ReferenceRegion_Right

Preprocessing consists of:
* n4 bias correction and
* brain extraction

Arguments
---------
t1 : ANTsImage
input image

verbose : boolean
Print progress to the screen.

Returns
-------
ANTs segmentation label and probability images.

Example
-------
>>> seg = cit168_labeling(t1)
"""

from ..utilities import get_pretrained_network
from ..utilities import preprocess_brain_image
from ..utilities import get_antsxnet_data
from ..architectures import create_unet_model_3d

if t1.dimension != 3:
raise ValueError( "Image dimension must be 3." )

template = ants.image_read(get_antsxnet_data("CIT168_T1w_700um_pad_adni"))
template_small = ants.resample_image(template, [2, 2, 2])
template_large = ants.resample_image(template, [0.5, 0.5, 0.5])
template_seg = ants.image_read(get_antsxnet_data("det_atlas_25_pad_LR_adni"))

template_transform_type = "antsRegistrationSyNQuickRepro[s]"

cropped_template_size = [160, 160, 112]

################################
#
# Preprocess images
#
################################

# clone input to float
t1 = ants.image_clone(t1, pixeltype="float")

t1_preprocessing = preprocess_brain_image(t1,
truncate_intensity=[1e-4, 0.999],
brain_extraction_modality="t1threetissue",
template=None,
do_bias_correction=True,
do_denoising=False,
intensity_normalization_type='01',
verbose=verbose)

t1_preprocessed = t1_preprocessing['preprocessed_image'] * t1_preprocessing['brain_mask']

reg = ants.registration(template_small, t1_preprocessed, type_of_transform=template_transform_type, verbose=verbose)
t1_preprocessed = ants.apply_transforms(fixed=template_large, moving=t1_preprocessed,
transformlist=reg['fwdtransforms'][1],
interpolator="linear", singleprecision=True, verbose=verbose)

template_priors = ants.apply_transforms(fixed=t1_preprocessed, moving=template_seg,
transformlist=reg['invtransforms'][1],
interpolator="nearestNeighbor", singleprecision=True, verbose=verbose)

center_of_mass = list(ants.get_center_of_mass(ants.threshold_image(template_priors, 0, 0, 0, 1)))
center_of_mass[1] = center_of_mass[1] + 10.0 # same as we did in training
t1_cropped = ants.crop_image_from_center_point(t1_preprocessed, center_of_mass, cropped_template_size)
template_priors_cropped = ants.crop_image_from_center_point(template_priors, center_of_mass, cropped_template_size)

################################
#
# Build model and load weights
#
################################


cit168_segmentation_image = t1 * 0
for sn in [True,False]:

if verbose:
print(" CIT168: Creating model and loading weights. (sn=" + str(sn) + ")")

if sn:
group_labels = [0,7,8,9,23,24,25,33,34]
cit168_weights = get_pretrained_network("deepCIT168_sn")
else:
group_labels = [0,1,2,5,6,17,18,21,22]
cit168_weights = get_pretrained_network("deepCIT168")

channel_size = 1
number_of_outputs = len(group_labels)
number_of_channels = len(group_labels)

unet0 = create_unet_model_3d([None, None, None, number_of_channels],
number_of_outputs=1, number_of_layers=4,
number_of_filters_at_base_layer=32,
convolution_kernel_size=3,
deconvolution_kernel_size=2,
pool_size=2, strides=2, dropout_rate=0.0,
weight_decay=0,
additional_options="nnUnetActivationStyle",
mode="sigmoid")

unet1 = create_unet_model_3d([None, None, None, 2],
number_of_outputs=number_of_outputs,
number_of_filters=(32, 64, 96, 128, 256),
convolution_kernel_size=(3, 3, 3),
deconvolution_kernel_size=(2, 2, 2),
dropout_rate=0.0, weight_decay=0,
additional_options = "nnUnetActivationStyle",
mode="classification")

splits = ops.split(unet0.inputs[0], 9, axis=-1)
newmult = layers.Concatenate(axis=-1)([splits[0], unet0.outputs[0]])
unetonnet = unet1(newmult)
unet_model = Model(inputs=unet0.inputs, outputs=[unetonnet, unet0.outputs[0]])

unet_model.load_weights(cit168_weights)

priors = ants.segmentation_to_one_hot(template_priors_cropped.numpy().astype('int'),
segmentation_labels=group_labels[1:len(group_labels)])
batchX = np.concatenate((np.expand_dims(t1_cropped.numpy(), axis=-1), priors), axis=-1)

if verbose:
print(" CIT168: Model prediction.")

predicted_data = unet_model.predict(np.expand_dims(batchX, axis=0), verbose=verbose)

probability_images = []
for i in range(1, len(group_labels)):
probability_array = np.squeeze(predicted_data[0][0,:,:,:,i])
probability_image = ants.from_numpy_like(probability_array, t1_cropped)
probability_image = ants.apply_transforms(fixed=t1, moving=probability_image,
transformlist=reg['invtransforms'][0], whichtoinvert=[True],
interpolator="linear", singleprecision=True, verbose=verbose)
probability_images.append(probability_image)

predicted_mask = ants.from_numpy_like(np.squeeze(predicted_data[1][0,:,:,:,0]), t1_cropped)
predicted_mask = ants.apply_transforms(fixed=t1, moving=predicted_mask,
transformlist=reg['invtransforms'][0], whichtoinvert=[True],
interpolator="linear", singleprecision=True, verbose=verbose)
predicted_mask = ants.threshold_image(predicted_mask, 0.5, 1, 1, 0)

image_matrix = ants.image_list_to_matrix(probability_images, predicted_mask)
segmentation_matrix = np.argmax(image_matrix, axis=0) + 1
segmentation_image = ants.matrix_to_images(
np.expand_dims(segmentation_matrix, axis=0), predicted_mask)[0]
relabeled_segmentation_image = segmentation_image * 0
for i in range(1, len(group_labels)):
single_label_image = ants.threshold_image(segmentation_image, i, i, 1, 0)
if group_labels[i] < 33:
single_label_image = ants.iMath_get_largest_component(single_label_image, 1)
relabeled_segmentation_image += single_label_image * group_labels[i]

cit168_segmentation_image += relabeled_segmentation_image

return cit168_segmentation_image

8 changes: 7 additions & 1 deletion antspynet/utilities/get_antsxnet_data.py
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Expand Up @@ -117,7 +117,10 @@ def switch_data(argument):
"DevCCF_P56_MRI-T2_50um_BrainParcellationNickMask": "https://ndownloader.figshare.com/files/44706238",
"DevCCF_P56_MRI-T2_50um_BrainParcellationTctMask": "https://ndownloader.figshare.com/files/47214532",
"DevCCF_P04_STPT_50um": "https://ndownloader.figshare.com/files/46711546",
"DevCCF_P04_STPT_50um_BrainParcellationJayMask": "https://ndownloader.figshare.com/files/46712656"
"DevCCF_P04_STPT_50um_BrainParcellationJayMask": "https://ndownloader.figshare.com/files/46712656",
"CIT168_T1w_700um_pad_adni": "https://ndownloader.figshare.com/files/64536261",
"CIT168_basal_forebrain_adni": "https://ndownloader.figshare.com/files/64536324",
"det_atlas_25_pad_LR_adni": "https://ndownloader.figshare.com/files/64536333",
}
return(switcher.get(argument, "Invalid argument."))

Expand Down Expand Up @@ -179,6 +182,9 @@ def switch_data(argument):
"hcpinterFATemplate",
"hcpinterTemplateBrainMask",
"hcpinterTemplateBrainSegmentation",
"CIT168_T1w_700um_pad_adni",
"CIT168_basal_forebrain_adni",
"det_atlas_25_pad_LR_adni",
"show")

if not file_id in valid_list:
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8 changes: 7 additions & 1 deletion antspynet/utilities/get_pretrained_network.py
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Expand Up @@ -187,7 +187,10 @@ def switch_networks(argument):
"sig_smallshort_train_2x2x2_1chan_featgraderL6_best_mdl": "https://ndownloader.figshare.com/files/49339858",
"sig_smallshort_train_2x2x2_1chan_featvggL6_best_mdl": "https://ndownloader.figshare.com/files/49339861",
"sig_smallshort_train_2x2x4_1chan_featgraderL6_best_mdl": "https://ndownloader.figshare.com/files/49339867",
"sig_smallshort_train_2x2x4_1chan_featvggL6_best_mdl": "https://ndownloader.figshare.com/files/49339864"
"sig_smallshort_train_2x2x4_1chan_featvggL6_best_mdl": "https://ndownloader.figshare.com/files/49339864",
"deep_nbm_rank": "https://ndownloader.figshare.com/files/64536405",
"deepCIT168": "https://ndownloader.figshare.com/files/64536618",
"deepCIT168_sn": "https://ndownloader.figshare.com/files/64537371"
}
return(switcher.get(argument, "Invalid argument."))

Expand Down Expand Up @@ -350,6 +353,9 @@ def switch_networks(argument):
"sig_smallshort_train_2x2x2_1chan_featvggL6_best_mdl",
"sig_smallshort_train_2x2x4_1chan_featgraderL6_best_mdl",
"sig_smallshort_train_2x2x4_1chan_featvggL6_best_mdl",
"deep_nbm_rank",
"deepCIT168",
"deepCIT168_sn",
"show")

if not file_id in valid_list:
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