diff --git a/scilpy/image/resample_volume.py b/scilpy/image/resample_volume.py
deleted file mode 100644
index 98ee3964f..000000000
--- a/scilpy/image/resample_volume.py
+++ /dev/null
@@ -1,119 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import logging
-
-from scilpy.image.reslice import reslice  # Don't use Dipy's reslice. Buggy.
-import nibabel as nib
-import numpy as np
-
-
-def _interp_code_to_order(interp_code):
-    orders = {'nn': 0, 'lin': 1, 'quad': 2, 'cubic': 3}
-    return orders[interp_code]
-
-
-def resample_volume(img, ref=None, res=None, iso_min=False, zoom=None,
-                    interp='lin', enforce_dimensions=False):
-    """
-    Function to resample a dataset to match the resolution of another
-    reference dataset or to the resolution specified as in argument.
-    One of the following options must be chosen: ref, res or iso_min.
-
-    Parameters
-    ----------
-    img: nib.Nifti1Image
-        Image to resample.
-    ref: nib.Nifti1Image
-        Reference volume to resample to. This method is used only if ref is not
-        None. (default: None)
-    res: tuple, shape (3,) or int, optional
-        Resolution to resample to. If the value it is set to is Y, it will
-        resample to an isotropic resolution of Y x Y x Y. This method is used
-        only if res is not None. (default: None)
-    iso_min: bool, optional
-        If true, resample the volume to R x R x R with R being the smallest
-        current voxel dimension. If false, this method is not used.
-    zoom: tuple, shape (3,) or float, optional
-        Set the zoom property of the image at the value specified.
-    interp: str, optional
-        Interpolation mode. 'nn' = nearest neighbour, 'lin' = linear,
-        'quad' = quadratic, 'cubic' = cubic. (Default: linear)
-    enforce_dimensions: bool, optional
-        If True, enforce the reference volume dimension (only if res is not
-        None). (Default = False)
-
-    Returns
-    -------
-    resampled_image: nib.Nifti1Image
-        Resampled image.
-    """
-    data = np.asanyarray(img.dataobj)
-    original_res = data.shape
-    affine = img.affine
-    original_zooms = img.header.get_zooms()[:3]
-
-    if ref is not None:
-        if iso_min or zoom or res:
-            raise ValueError('Please only provide one option amongst ref, res '
-                             ', zoom or iso_min.')
-        ref_img = nib.load(ref)
-        new_zooms = ref_img.header.get_zooms()[:3]
-    elif res is not None:
-        if iso_min or zoom:
-            raise ValueError('Please only provide one option amongst ref, res '
-                             ', zoom or iso_min.')
-        if len(res) == 1:
-            res = res * 3
-        new_zooms = tuple((o / r) * z for o, r,
-                          z in zip(original_res, res, original_zooms))
-
-    elif iso_min:
-        if zoom:
-            raise ValueError('Please only provide one option amongst ref, res '
-                             ', zoom or iso_min.')
-        min_zoom = min(original_zooms)
-        new_zooms = (min_zoom, min_zoom, min_zoom)
-    elif zoom:
-        new_zooms = zoom
-        if len(zoom) == 1:
-            new_zooms = zoom * 3
-    else:
-        raise ValueError("You must choose the resampling method. Either with"
-                         "a reference volume, or a chosen isometric resolution"
-                         ", or an isometric resampling to the smallest current"
-                         " voxel dimension!")
-
-    interp_choices = ['nn', 'lin', 'quad', 'cubic']
-    if interp not in interp_choices:
-        raise ValueError("interp must be one of 'nn', 'lin', 'quad', 'cubic'.")
-
-    logging.debug('Data shape: %s', data.shape)
-    logging.debug('Data affine: %s', affine)
-    logging.debug('Data affine setup: %s', nib.aff2axcodes(affine))
-    logging.debug('Resampling data to %s with mode %s', new_zooms, interp)
-
-    data2, affine2 = reslice(data, affine, original_zooms, new_zooms,
-                             _interp_code_to_order(interp))
-
-    logging.debug('Resampled data shape: %s', data2.shape)
-    logging.debug('Resampled data affine: %s', affine2)
-    logging.debug('Resampled data affine setup: %s', nib.aff2axcodes(affine2))
-
-    if enforce_dimensions:
-        if ref is None:
-            raise ValueError('enforce_dimensions can only be used with the ref'
-                             'method.')
-        else:
-            computed_dims = data2.shape
-            ref_dims = ref_img.shape[:3]
-            if computed_dims != ref_dims:
-                fix_dim_volume = np.zeros(ref_dims)
-                x_dim = min(computed_dims[0], ref_dims[0])
-                y_dim = min(computed_dims[1], ref_dims[1])
-                z_dim = min(computed_dims[2], ref_dims[2])
-
-                fix_dim_volume[:x_dim, :y_dim, :z_dim] = \
-                    data2[:x_dim, :y_dim, :z_dim]
-                data2 = fix_dim_volume
-
-    return nib.Nifti1Image(data2.astype(data.dtype), affine2)
diff --git a/scilpy/image/utils.py b/scilpy/image/utils.py
index 4b7b4939a..189e3db78 100644
--- a/scilpy/image/utils.py
+++ b/scilpy/image/utils.py
@@ -4,37 +4,9 @@
 
 import nibabel as nib
 import numpy as np
-import six
 from sklearn.cluster import KMeans
 
 
-def count_non_zero_voxels(image):
-    """
-    Count number of non zero voxels
-
-    Parameters:
-    -----------
-    image: string
-        Path to the image
-    """
-    if isinstance(image, six.string_types):
-        nb_object = nib.load(image)
-    else:
-        nb_object = image
-
-    data = nb_object.get_fdata(dtype=np.float32, caching='unchanged')
-
-    # Count the number of non-zero voxels.
-    if len(data.shape) >= 4:
-        axes_to_sum = np.arange(3, len(data.shape))
-        nb_voxels = np.count_nonzero(np.sum(np.absolute(data),
-                                            axis=tuple(axes_to_sum)))
-    else:
-        nb_voxels = np.count_nonzero(data)
-
-    return nb_voxels
-
-
 def volume_iterator(img, blocksize=1, start=0, end=0):
     """Generator that iterates on volumes of data.
 
diff --git a/scilpy/image/operations.py b/scilpy/image/volume_math.py
similarity index 100%
rename from scilpy/image/operations.py
rename to scilpy/image/volume_math.py
diff --git a/scilpy/utils/image.py b/scilpy/image/volume_operations.py
similarity index 57%
rename from scilpy/utils/image.py
rename to scilpy/image/volume_operations.py
index de8812e7a..dd5ab0c41 100644
--- a/scilpy/utils/image.py
+++ b/scilpy/image/volume_operations.py
@@ -10,17 +10,84 @@
                                    RigidTransform3D)
 from dipy.io.gradients import read_bvals_bvecs
 from dipy.io.utils import get_reference_info
-from dipy.segment.mask import median_otsu
+from dipy.segment.mask import crop, median_otsu
 import nibabel as nib
 import numpy as np
 
+from scilpy.image.reslice import reslice  # Don't use Dipy's reslice. Buggy.
 from scipy.ndimage import binary_dilation
+
 from scilpy.io.image import get_data_as_mask
 from scilpy.utils.bvec_bval_tools import identify_shells
+from scilpy.utils.util import voxel_to_world, world_to_voxel
+
+
+def count_non_zero_voxels(image):
+    """
+    Count number of non-zero voxels
+
+    Parameters:
+    -----------
+    image: string
+        Path to the image
+    """
+    # Count the number of non-zero voxels.
+    if len(image.shape) >= 4:
+        axes_to_sum = np.arange(3, len(image.shape))
+        nb_voxels = np.count_nonzero(np.sum(np.absolute(image),
+                                            axis=tuple(axes_to_sum)))
+    else:
+        nb_voxels = np.count_nonzero(image)
+
+    return nb_voxels
 
 
-def transform_anatomy(transfo, reference, moving, filename_to_save,
-                      interp='linear', keep_dtype=False):
+def flip_volume(data, axes):
+    """
+    data: np.ndarray
+    axes: a list containing any number of values amongst ['x', 'y', 'z'].
+    """
+    if 'x' in axes:
+        data = data[::-1, ...]
+
+    if 'y' in axes:
+        data = data[:, ::-1, ...]
+
+    if 'z' in axes:
+        data = data[:, :, ::-1, ...]
+
+    return data
+
+
+def crop_volume(img: nib.Nifti1Image, wbbox):
+    """Applies cropping from a world space defined bounding box and fixes the
+    affine to keep data aligned.
+
+    wbbox: WorldBoundingBox from the scrip scil_crop_volume. ToDo. Update this.
+    """
+    data = img.get_fdata(dtype=np.float32, caching='unchanged')
+    affine = img.affine
+
+    voxel_bb_mins = world_to_voxel(wbbox.minimums, affine)
+    voxel_bb_maxs = world_to_voxel(wbbox.maximums, affine)
+
+    # Prevent from trying to crop outside data boundaries by clipping bbox
+    extent = list(data.shape[:3])
+    for i in range(3):
+        voxel_bb_mins[i] = max(0, voxel_bb_mins[i])
+        voxel_bb_maxs[i] = min(extent[i], voxel_bb_maxs[i])
+    translation = voxel_to_world(voxel_bb_mins, affine)
+
+    data_crop = np.copy(crop(data, voxel_bb_mins, voxel_bb_maxs))
+
+    new_affine = np.copy(affine)
+    new_affine[0:3, 3] = translation[0:3]
+
+    return nib.Nifti1Image(data_crop, new_affine)
+
+
+def apply_transform(transfo, reference, moving, filename_to_save,
+                    interp='linear', keep_dtype=False):
     """
     Apply transformation to an image using Dipy's tool
 
@@ -243,3 +310,115 @@ def compute_snr(dwi, bval, bvec, b0_thr, mask,
         val[idx]['snr'] = val[idx]['mean'] / val[idx]['std']
 
     return val
+
+
+def _interp_code_to_order(interp_code):
+    orders = {'nn': 0, 'lin': 1, 'quad': 2, 'cubic': 3}
+    return orders[interp_code]
+
+
+def resample_volume(img, ref=None, res=None, iso_min=False, zoom=None,
+                    interp='lin', enforce_dimensions=False):
+    """
+    Function to resample a dataset to match the resolution of another
+    reference dataset or to the resolution specified as in argument.
+    One of the following options must be chosen: ref, res or iso_min.
+
+    Parameters
+    ----------
+    img: nib.Nifti1Image
+        Image to resample.
+    ref: nib.Nifti1Image
+        Reference volume to resample to. This method is used only if ref is not
+        None. (default: None)
+    res: tuple, shape (3,) or int, optional
+        Resolution to resample to. If the value it is set to is Y, it will
+        resample to an isotropic resolution of Y x Y x Y. This method is used
+        only if res is not None. (default: None)
+    iso_min: bool, optional
+        If true, resample the volume to R x R x R with R being the smallest
+        current voxel dimension. If false, this method is not used.
+    zoom: tuple, shape (3,) or float, optional
+        Set the zoom property of the image at the value specified.
+    interp: str, optional
+        Interpolation mode. 'nn' = nearest neighbour, 'lin' = linear,
+        'quad' = quadratic, 'cubic' = cubic. (Default: linear)
+    enforce_dimensions: bool, optional
+        If True, enforce the reference volume dimension (only if res is not
+        None). (Default = False)
+
+    Returns
+    -------
+    resampled_image: nib.Nifti1Image
+        Resampled image.
+    """
+    data = np.asanyarray(img.dataobj)
+    original_res = data.shape
+    affine = img.affine
+    original_zooms = img.header.get_zooms()[:3]
+
+    if ref is not None:
+        if iso_min or zoom or res:
+            raise ValueError('Please only provide one option amongst ref, res '
+                             ', zoom or iso_min.')
+        ref_img = nib.load(ref)
+        new_zooms = ref_img.header.get_zooms()[:3]
+    elif res is not None:
+        if iso_min or zoom:
+            raise ValueError('Please only provide one option amongst ref, res '
+                             ', zoom or iso_min.')
+        if len(res) == 1:
+            res = res * 3
+        new_zooms = tuple((o / r) * z for o, r,
+                          z in zip(original_res, res, original_zooms))
+
+    elif iso_min:
+        if zoom:
+            raise ValueError('Please only provide one option amongst ref, res '
+                             ', zoom or iso_min.')
+        min_zoom = min(original_zooms)
+        new_zooms = (min_zoom, min_zoom, min_zoom)
+    elif zoom:
+        new_zooms = zoom
+        if len(zoom) == 1:
+            new_zooms = zoom * 3
+    else:
+        raise ValueError("You must choose the resampling method. Either with"
+                         "a reference volume, or a chosen isometric resolution"
+                         ", or an isometric resampling to the smallest current"
+                         " voxel dimension!")
+
+    interp_choices = ['nn', 'lin', 'quad', 'cubic']
+    if interp not in interp_choices:
+        raise ValueError("interp must be one of 'nn', 'lin', 'quad', 'cubic'.")
+
+    logging.debug('Data shape: %s', data.shape)
+    logging.debug('Data affine: %s', affine)
+    logging.debug('Data affine setup: %s', nib.aff2axcodes(affine))
+    logging.debug('Resampling data to %s with mode %s', new_zooms, interp)
+
+    data2, affine2 = reslice(data, affine, original_zooms, new_zooms,
+                             _interp_code_to_order(interp))
+
+    logging.debug('Resampled data shape: %s', data2.shape)
+    logging.debug('Resampled data affine: %s', affine2)
+    logging.debug('Resampled data affine setup: %s', nib.aff2axcodes(affine2))
+
+    if enforce_dimensions:
+        if ref is None:
+            raise ValueError('enforce_dimensions can only be used with the ref'
+                             'method.')
+        else:
+            computed_dims = data2.shape
+            ref_dims = ref_img.shape[:3]
+            if computed_dims != ref_dims:
+                fix_dim_volume = np.zeros(ref_dims)
+                x_dim = min(computed_dims[0], ref_dims[0])
+                y_dim = min(computed_dims[1], ref_dims[1])
+                z_dim = min(computed_dims[2], ref_dims[2])
+
+                fix_dim_volume[:x_dim, :y_dim, :z_dim] = \
+                    data2[:x_dim, :y_dim, :z_dim]
+                data2 = fix_dim_volume
+
+    return nib.Nifti1Image(data2.astype(data.dtype), affine2)
diff --git a/scilpy/image/datasets.py b/scilpy/image/volume_space_management.py
similarity index 99%
rename from scilpy/image/datasets.py
rename to scilpy/image/volume_space_management.py
index b952090f1..b112912e4 100644
--- a/scilpy/image/datasets.py
+++ b/scilpy/image/volume_space_management.py
@@ -4,6 +4,10 @@
 from dipy.core.interpolation import trilinear_interpolate4d, \
     nearestneighbor_interpolate
 from dipy.io.stateful_tractogram import Origin, Space
+from dipy.segment.mask import bounding_box
+
+
+from scilpy.utils.util import voxel_to_world
 
 
 class DataVolume(object):
diff --git a/scilpy/tracking/propagator.py b/scilpy/tracking/propagator.py
index 4793c95a6..bd4ddddd4 100644
--- a/scilpy/tracking/propagator.py
+++ b/scilpy/tracking/propagator.py
@@ -29,11 +29,11 @@ class AbstractPropagator(object):
     Propagation depends on the type of data (ex, DTI, fODF) and the way to get
     a direction from it (ex, det, prob).
     """
-    def __init__(self, dataset, step_size, rk_order,  space, origin):
+    def __init__(self, datavolume, step_size, rk_order, space, origin):
         """
         Parameters
         ----------
-        dataset: scilpy.image.datasets.DataVolume
+        datavolume: scilpy.image.volume_space_management.DataVolume
             Trackable Dataset object.
         step_size: float
             The step size for tracking. Important: step size should be in the
@@ -52,7 +52,7 @@ def __init__(self, dataset, step_size, rk_order,  space, origin):
         Tracker will verify that the propagator has the same internal values as
         itself.
         """
-        self.dataset = dataset
+        self.datavolume = datavolume
 
         self.origin = origin
         self.space = space
@@ -76,10 +76,10 @@ def reset_data(self, new_data=None):
         Params
         ------
         new_data: Any
-            Will replace self.dataset.data.
+            Will replace self.datavolume.data.
 
         """
-        self.dataset.data = new_data
+        self.datavolume.data = new_data
 
     def prepare_forward(self, seeding_pos):
         """
@@ -250,13 +250,13 @@ def _sample_next_direction(self, pos, v_in):
 
 
 class PropagatorOnSphere(AbstractPropagator):
-    def __init__(self, dataset, step_size, rk_order, dipy_sphere,
+    def __init__(self, datavolume, step_size, rk_order, dipy_sphere,
                  space, origin):
         """
         Parameters
         ----------
-        dataset: scilpy.image.datasets.DataVolume
-            Trackable Dataset object.
+        datavolume: scilpy.image.volume_space_management.DataVolume
+            Trackable DataVolume object.
         step_size: float
             The step size for tracking.
         rk_order: int
@@ -269,7 +269,7 @@ def __init__(self, dataset, step_size, rk_order, dipy_sphere,
         origin: dipy Origin
             Origin of the streamlines during tracking.
         """
-        super().__init__(dataset, step_size, rk_order, space, origin)
+        super().__init__(datavolume, step_size, rk_order, space, origin)
 
         self.sphere = dipy.data.get_sphere(dipy_sphere)
         self.dirs = np.zeros(len(self.sphere.vertices), dtype=np.ndarray)
@@ -312,7 +312,7 @@ class ODFPropagator(PropagatorOnSphere):
     """
     Propagator on ODFs/fODFs. Algo can be det or prob.
     """
-    def __init__(self, dataset, step_size,
+    def __init__(self, datavolume, step_size,
                  rk_order, algo, basis, sf_threshold, sf_threshold_init,
                  theta, dipy_sphere='symmetric724',
                  min_separation_angle=np.pi / 16.,
@@ -321,8 +321,8 @@ def __init__(self, dataset, step_size,
 
         Parameters
         ----------
-        dataset: scilpy.image.datasets.DataVolume
-            Trackable Dataset object.
+        datavolume: scilpy.image.volume_space_management.DataVolume
+            Trackable DataVolume object.
         step_size: float
             The step size for tracking.
         rk_order: int
@@ -360,7 +360,7 @@ def __init__(self, dataset, step_size,
             dipy. Interpolation of the ODF is done in center origin so this
             choice implies the less data modification.
         """
-        super().__init__(dataset, step_size, rk_order, dipy_sphere,
+        super().__init__(datavolume, step_size, rk_order, dipy_sphere,
                          space, origin)
 
         if self.space == Space.RASMM:
@@ -389,7 +389,7 @@ def __init__(self, dataset, step_size,
         self.sf_threshold = sf_threshold
         self.sf_threshold_init = sf_threshold_init
         sh_order, full_basis =\
-            get_sh_order_and_fullness(self.dataset.data.shape[-1])
+            get_sh_order_and_fullness(self.datavolume.data.shape[-1])
         self.basis = basis
         self.B = sh_to_sf_matrix(self.sphere, sh_order, self.basis,
                                  smooth=0.006, return_inv=False,
@@ -412,7 +412,7 @@ def _get_sf(self, pos):
             its maximum amplitude.
         """
         # Interpolation:
-        sh = self.dataset.get_value_at_coordinate(
+        sh = self.datavolume.get_value_at_coordinate(
             *pos, space=self.space, origin=self.origin)
         sf = np.dot(self.B.T, sh).reshape((-1, 1))
 
diff --git a/scilpy/tracking/tracker.py b/scilpy/tracking/tracker.py
index 38f671a02..1873af41e 100644
--- a/scilpy/tracking/tracker.py
+++ b/scilpy/tracking/tracker.py
@@ -17,7 +17,7 @@
 from dipy.reconst.shm import sh_to_sf_matrix
 from dipy.tracking.streamlinespeed import compress_streamlines
 
-from scilpy.image.datasets import DataVolume
+from scilpy.image.volume_space_management import DataVolume
 from scilpy.tracking.propagator import AbstractPropagator, PropagationStatus
 from scilpy.reconst.utils import find_order_from_nb_coeff
 from scilpy.tracking.seed import SeedGenerator
@@ -207,7 +207,7 @@ def _prepare_multiprocessing_pool(self, tmpdir):
 
         # Saving data. We will reload it in each process.
         data_file_name = os.path.join(tmpdir, 'data.npy')
-        np.save(data_file_name, self.propagator.dataset.data)
+        np.save(data_file_name, self.propagator.datavolume.data)
 
         # Clear data from memory
         self.propagator.reset_data(new_data=None)
diff --git a/scripts/scil_apply_transform_to_bvecs.py b/scripts/scil_apply_transform_to_bvecs.py
index d8f926d7e..bbfe26c20 100755
--- a/scripts/scil_apply_transform_to_bvecs.py
+++ b/scripts/scil_apply_transform_to_bvecs.py
@@ -13,8 +13,6 @@
 
 from scilpy.io.utils import (add_overwrite_arg, assert_inputs_exist,
                              assert_outputs_exist, load_matrix_in_any_format)
-from scilpy.utils.filenames import split_name_with_nii
-from scilpy.utils.image import transform_anatomy
 
 
 def _build_arg_parser():
diff --git a/scripts/scil_apply_transform_to_image.py b/scripts/scil_apply_transform_to_image.py
index db5ce72eb..83f5ce0c9 100755
--- a/scripts/scil_apply_transform_to_image.py
+++ b/scripts/scil_apply_transform_to_image.py
@@ -15,7 +15,7 @@
 from scilpy.io.utils import (add_overwrite_arg, assert_inputs_exist,
                              assert_outputs_exist, load_matrix_in_any_format)
 from scilpy.utils.filenames import split_name_with_nii
-from scilpy.utils.image import transform_anatomy
+from scilpy.image.volume_operations import apply_transform
 
 
 def _build_arg_parser():
@@ -63,8 +63,8 @@ def main():
     if in_extension not in ['.nii', '.nii.gz']:
         parser.error('{} is an unsupported format.'.format(args.in_file))
 
-    transform_anatomy(transfo, args.in_target_file, args.in_file,
-                      args.out_name, keep_dtype=args.keep_dtype)
+    apply_transform(transfo, args.in_target_file, args.in_file,
+                    args.out_name, keep_dtype=args.keep_dtype)
 
 
 if __name__ == "__main__":
diff --git a/scripts/scil_compute_bundle_voxel_label_map.py b/scripts/scil_compute_bundle_voxel_label_map.py
index 232cab1db..70b81682d 100755
--- a/scripts/scil_compute_bundle_voxel_label_map.py
+++ b/scripts/scil_compute_bundle_voxel_label_map.py
@@ -22,7 +22,7 @@
 import scipy.ndimage as ndi
 from scipy.spatial import cKDTree
 
-from scilpy.image.operations import correlation
+from scilpy.image.volume_math import correlation
 from scilpy.io.streamlines import load_tractogram_with_reference
 from scilpy.io.utils import (add_overwrite_arg,
                              add_reference_arg,
diff --git a/scripts/scil_compute_local_tracking_dev.py b/scripts/scil_compute_local_tracking_dev.py
index 51b59b025..4157631a6 100755
--- a/scripts/scil_compute_local_tracking_dev.py
+++ b/scripts/scil_compute_local_tracking_dev.py
@@ -59,7 +59,7 @@
                              add_verbose_arg,
                              assert_inputs_exist, assert_outputs_exist,
                              verify_compression_th)
-from scilpy.image.datasets import DataVolume
+from scilpy.image.volume_space_management import DataVolume
 from scilpy.tracking.propagator import ODFPropagator
 from scilpy.tracking.seed import SeedGenerator
 from scilpy.tracking.tools import get_theta
diff --git a/scripts/scil_connectivity_math.py b/scripts/scil_connectivity_math.py
index e7c275b02..b1b0deb06 100755
--- a/scripts/scil_connectivity_math.py
+++ b/scripts/scil_connectivity_math.py
@@ -17,7 +17,7 @@
 import nibabel as nib
 import numpy as np
 
-from scilpy.image.operations import (get_array_ops, get_operations_doc)
+from scilpy.image.volume_math import (get_array_ops, get_operations_doc)
 from scilpy.io.utils import (add_overwrite_arg,
                              add_verbose_arg,
                              assert_outputs_exist,
diff --git a/scripts/scil_count_non_zero_voxels.py b/scripts/scil_count_non_zero_voxels.py
index d04b8824a..ebf252ca2 100755
--- a/scripts/scil_count_non_zero_voxels.py
+++ b/scripts/scil_count_non_zero_voxels.py
@@ -13,7 +13,10 @@
 import argparse
 import os
 
-from scilpy.image.utils import count_non_zero_voxels
+import nibabel as nib
+import numpy as np
+
+from scilpy.image.volume_operations import count_non_zero_voxels
 from scilpy.io.utils import assert_inputs_exist
 
 
@@ -48,7 +51,9 @@ def main():
     # out_filename can exist or not
 
     # Load image file
-    nb_voxels = count_non_zero_voxels(args.in_image)
+    im = nib.load(args.in_image).get_fdata(dtype=np.float32)
+
+    nb_voxels = count_non_zero_voxels(im)
 
     if args.out_filename is not None:
         open_mode = 'w'
diff --git a/scripts/scil_filter_connectivity.py b/scripts/scil_filter_connectivity.py
index 026f23193..61752a118 100755
--- a/scripts/scil_filter_connectivity.py
+++ b/scripts/scil_filter_connectivity.py
@@ -36,7 +36,7 @@
 
 import numpy as np
 
-from scilpy.image.operations import invert
+from scilpy.image.volume_math import invert
 from scilpy.io.utils import (add_overwrite_arg, add_verbose_arg,
                              assert_outputs_exist,
                              load_matrix_in_any_format,
diff --git a/scripts/scil_flip_volume.py b/scripts/scil_flip_volume.py
index f3b366d04..e0bb96f89 100755
--- a/scripts/scil_flip_volume.py
+++ b/scripts/scil_flip_volume.py
@@ -9,6 +9,7 @@
 import nibabel as nib
 import numpy as np
 
+from scilpy.image.volume_operations import flip_volume
 from scilpy.io.utils import (add_overwrite_arg, assert_inputs_exist,
                              assert_outputs_exist)
 
@@ -42,14 +43,7 @@ def main():
     affine = vol.affine
     header = vol.header
 
-    if 'x' in args.axes:
-        data = data[::-1, ...]
-
-    if 'y' in args.axes:
-        data = data[:, ::-1, ...]
-
-    if 'z' in args.axes:
-        data = data[:, :, ::-1, ...]
+    data = flip_volume(data, args.axes)
 
     nib.save(nib.Nifti1Image(data, affine, header=header), args.out_image)
 
diff --git a/scripts/scil_image_math.py b/scripts/scil_image_math.py
index 26a4fb419..4819280c7 100755
--- a/scripts/scil_image_math.py
+++ b/scripts/scil_image_math.py
@@ -21,7 +21,7 @@
 import nibabel as nib
 import numpy as np
 
-from scilpy.image.operations import (get_image_ops, get_operations_doc)
+from scilpy.image.volume_math import (get_image_ops, get_operations_doc)
 from scilpy.io.utils import (add_overwrite_arg,
                              add_verbose_arg,
                              assert_outputs_exist)
diff --git a/scripts/scil_normalize_connectivity.py b/scripts/scil_normalize_connectivity.py
index ff86ccea2..4c4b49d07 100755
--- a/scripts/scil_normalize_connectivity.py
+++ b/scripts/scil_normalize_connectivity.py
@@ -49,7 +49,7 @@
 import numpy as np
 
 from scilpy.image.labels import get_data_as_labels
-from scilpy.image.operations import normalize_max, normalize_sum, base_10_log
+from scilpy.image.volume_math import normalize_max, normalize_sum, base_10_log
 from scilpy.io.utils import (add_overwrite_arg,
                              assert_inputs_exist,
                              assert_outputs_exist,
diff --git a/scripts/scil_resample_volume.py b/scripts/scil_resample_volume.py
index ce61da256..88ac7d0b4 100755
--- a/scripts/scil_resample_volume.py
+++ b/scripts/scil_resample_volume.py
@@ -13,7 +13,7 @@
 
 from scilpy.io.utils import (add_verbose_arg, add_overwrite_arg,
                              assert_inputs_exist, assert_outputs_exist)
-from scilpy.image.resample_volume import resample_volume
+from scilpy.image.volume_operations import resample_volume
 
 
 def _build_arg_parser():
diff --git a/scripts/scil_reshape_to_reference.py b/scripts/scil_reshape_to_reference.py
index a9b62aaf5..4d04a8555 100755
--- a/scripts/scil_reshape_to_reference.py
+++ b/scripts/scil_reshape_to_reference.py
@@ -17,7 +17,7 @@
 
 from scilpy.io.utils import (add_overwrite_arg, assert_inputs_exist,
                              assert_outputs_exist)
-from scilpy.utils.image import transform_anatomy
+from scilpy.image.volume_operations import apply_transform
 
 
 def _build_arg_parser():
@@ -50,9 +50,9 @@ def main():
     assert_inputs_exist(parser, [args.in_file, args.in_ref_file])
     assert_outputs_exist(parser, args, args.out_file)
 
-    transform_anatomy(np.eye(4), args.in_ref_file, args.in_file,
-                      args.out_file, interp=args.interpolation,
-                      keep_dtype=args.keep_dtype)
+    apply_transform(np.eye(4), args.in_ref_file, args.in_file,
+                    args.out_file, interp=args.interpolation,
+                    keep_dtype=args.keep_dtype)
 
 
 if __name__ == "__main__":
diff --git a/scripts/scil_screenshot_bundle.py b/scripts/scil_screenshot_bundle.py
index d61a52dd9..137bc593f 100755
--- a/scripts/scil_screenshot_bundle.py
+++ b/scripts/scil_screenshot_bundle.py
@@ -36,7 +36,7 @@
                              add_verbose_arg,
                              assert_inputs_exist,
                              assert_outputs_exist)
-from scilpy.utils.image import register_image
+from scilpy.image.volume_operations import register_image
 from scilpy.viz.screenshot import display_slices
 from scilpy.viz.utils import get_colormap
 
diff --git a/scripts/scil_screenshot_dti.py b/scripts/scil_screenshot_dti.py
index de81a233d..83dae6856 100755
--- a/scripts/scil_screenshot_dti.py
+++ b/scripts/scil_screenshot_dti.py
@@ -23,7 +23,7 @@
                              assert_inputs_exist,
                              assert_outputs_exist)
 from scilpy.utils.bvec_bval_tools import normalize_bvecs, get_shell_indices
-from scilpy.utils.image import register_image
+from scilpy.image.volume_operations import register_image
 from scilpy.viz.screenshot import display_slices
 
 
diff --git a/scripts/scil_snr_in_roi.py b/scripts/scil_snr_in_roi.py
index 93a4ac34c..70a2fe4fd 100755
--- a/scripts/scil_snr_in_roi.py
+++ b/scripts/scil_snr_in_roi.py
@@ -36,7 +36,7 @@
                              add_verbose_arg,
                              assert_inputs_exist)
 from scilpy.utils.filenames import split_name_with_nii
-from scilpy.utils.image import compute_snr
+from scilpy.image.volume_operations import compute_snr
 
 
 def _build_arg_parser():
diff --git a/scripts/scil_visualize_connectivity.py b/scripts/scil_visualize_connectivity.py
index f490a0324..4913e1b8a 100755
--- a/scripts/scil_visualize_connectivity.py
+++ b/scripts/scil_visualize_connectivity.py
@@ -36,7 +36,7 @@
 from matplotlib.font_manager import FontProperties
 import numpy as np
 
-from scilpy.image.operations import EPSILON
+from scilpy.image.volume_math import EPSILON
 from scilpy.io.utils import (add_overwrite_arg, assert_inputs_exist,
                              assert_outputs_exist, load_matrix_in_any_format)
 from scilpy.viz.chord_chart import chordDiagram, polar2xy