Source code for ibllib.atlas.atlas

from dataclasses import dataclass
import logging
import matplotlib.pyplot as plt
from pathlib import Path, PurePosixPath

import numpy as np
import nrrd

from iblutil.numerical import ismember
from ibllib.atlas.regions import BrainRegions
from one.webclient import http_download_file
import one.params

_logger = logging.getLogger('ibllib')
ALLEN_CCF_LANDMARKS_MLAPDV_UM = {'bregma': np.array([5739, 5400, 332])}


[docs]def cart2sph(x, y, z): """ Converts cartesian to spherical Coordinates theta: polar angle, phi: azimuth """ r = np.sqrt(x ** 2 + y ** 2 + z ** 2) phi = np.arctan2(y, x) * 180 / np.pi theta = np.zeros_like(r) iok = r != 0 theta[iok] = np.arccos(z[iok] / r[iok]) * 180 / np.pi if theta.size == 1: theta = float(theta) return r, theta, phi
[docs]def sph2cart(r, theta, phi): """ Converts Spherical to Cartesian coordinates theta: polar angle, phi: azimuth """ x = r * np.cos(phi / 180 * np.pi) * np.sin(theta / 180 * np.pi) y = r * np.sin(phi / 180 * np.pi) * np.sin(theta / 180 * np.pi) z = r * np.cos(theta / 180 * np.pi) return x, y, z
[docs]class BrainCoordinates: """ Class for mapping and indexing a 3D array to real-world coordinates x = ml, right positive y = ap, anterior positive z = dv, dorsal positive The layout of the Atlas dimension is done according to the most used sections so they lay contiguous on disk assuming C-ordering: V[iap, iml, idv] nxyz: number of elements along each cartesian axis (nx, ny, nz) = (nml, nap, ndv) xyz0: coordinates of the element volume[0, 0, 0]] in the coordinate space dxyz: spatial interval of the volume along the 3 dimensions """ def __init__(self, nxyz, xyz0=[0, 0, 0], dxyz=[1, 1, 1]): if np.isscalar(dxyz): dxyz = [dxyz for i in range(3)] self.x0, self.y0, self.z0 = list(xyz0) self.dx, self.dy, self.dz = list(dxyz) self.nx, self.ny, self.nz = list(nxyz) @property def dxyz(self): return np.array([self.dx, self.dy, self.dz]) @property def nxyz(self): return np.array([self.nx, self.ny, self.nz]) """Methods ratios to indice"""
[docs] def r2ix(self, r): return int((self.nx - 1) * r)
[docs] def r2iy(self, r): return int((self.nz - 1) * r)
[docs] def r2iz(self, r): return int((self.nz - 1) * r)
"""Methods distance to indice""" @staticmethod def _round(i, round=True): nanval = 0 if round: ii = np.array(np.round(i)).astype(int) ii[np.isnan(i)] = nanval return ii else: return i
[docs] def x2i(self, x, round=True): return self._round((x - self.x0) / self.dx, round=round)
[docs] def y2i(self, y, round=True): return self._round((y - self.y0) / self.dy, round=round)
[docs] def z2i(self, z, round=True): return self._round((z - self.z0) / self.dz, round=round)
[docs] def xyz2i(self, xyz, round=True): xyz = np.array(xyz) dt = int if round else float out = np.zeros_like(xyz, dtype=dt) out[..., 0] = self.x2i(xyz[..., 0], round=round) out[..., 1] = self.y2i(xyz[..., 1], round=round) out[..., 2] = self.z2i(xyz[..., 2], round=round) return out
"""Methods indices to distance"""
[docs] def i2x(self, ind): return ind * self.dx + self.x0
[docs] def i2y(self, ind): return ind * self.dy + self.y0
[docs] def i2z(self, ind): return ind * self.dz + self.z0
[docs] def i2xyz(self, iii): iii = np.array(iii) out = np.zeros_like(iii) out[..., 0] = self.i2x(iii[..., 0]) out[..., 1] = self.i2y(iii[..., 1]) out[..., 2] = self.i2z(iii[..., 2]) return out
"""Methods bounds""" @property def xlim(self): return self.i2x(np.array([0, self.nx - 1])) @property def ylim(self): return self.i2y(np.array([0, self.ny - 1])) @property def zlim(self): return self.i2z(np.array([0, self.nz - 1]))
[docs] def lim(self, axis): if axis == 0: return self.xlim elif axis == 1: return self.ylim elif axis == 2: return self.zlim
"""returns scales""" @property def xscale(self): return self.i2x(np.arange(self.nx)) @property def yscale(self): return self.i2y(np.arange(self.ny)) @property def zscale(self): return self.i2z(np.arange(self.nz)) """returns the 3d mgrid used for 3d visualization""" @property def mgrid(self): return np.meshgrid(self.xscale, self.yscale, self.zscale)
[docs]class BrainAtlas: """ Objects that holds image, labels and coordinate transforms for a brain Atlas. Currently this is designed for the AllenCCF at several resolutions, yet this class can be used for other atlases arises. """ def __init__(self, image, label, dxyz, regions, iorigin=[0, 0, 0], dims2xyz=[0, 1, 2], xyz2dims=[0, 1, 2]): """ self.image: image volume (ap, ml, dv) self.label: label volume (ap, ml, dv) self.bc: atlas.BrainCoordinate object self.regions: atlas.BrainRegions object self.top: 2d np array (ap, ml) containing the z-coordinate (m) of the surface of the brain self.dims2xyz and self.zyz2dims: map image axis order to xyz coordinates order """ self.image = image self.label = label self.regions = regions self.dims2xyz = dims2xyz self.xyz2dims = xyz2dims assert(np.all(self.dims2xyz[self.xyz2dims] == np.array([0, 1, 2]))) assert(np.all(self.xyz2dims[self.dims2xyz] == np.array([0, 1, 2]))) # create the coordinate transform object that maps volume indices to real world coordinates nxyz = np.array(self.image.shape)[self.dims2xyz] bc = BrainCoordinates(nxyz=nxyz, xyz0=(0, 0, 0), dxyz=dxyz) self.bc = BrainCoordinates(nxyz=nxyz, xyz0=- bc.i2xyz(iorigin), dxyz=dxyz) """ Get the volume top, bottom, left and right surfaces, and from these the outer surface of the image volume. This is needed to compute probe insertions intersections """ axz = self.xyz2dims[2] # this is the dv axis _surface = (self.label == 0).astype(np.int8) * 2 l0 = np.diff(_surface, axis=axz, append=2) _top = np.argmax(l0 == -2, axis=axz).astype(float) _top[_top == 0] = np.nan _bottom = self.bc.nz - np.argmax(np.flip(l0, axis=axz) == 2, axis=axz).astype(float) _bottom[_bottom == self.bc.nz] = np.nan self.top = self.bc.i2z(_top + 1) self.bottom = self.bc.i2z(_bottom - 1) self.surface = np.diff(_surface, axis=self.xyz2dims[0], append=2) + l0 idx_srf = np.where(self.surface != 0) self.surface[idx_srf] = 1 self.srf_xyz = self.bc.i2xyz(np.c_[idx_srf[self.xyz2dims[0]], idx_srf[self.xyz2dims[1]], idx_srf[self.xyz2dims[2]]].astype(float)) def _lookup_inds(self, ixyz): """ Performs a 3D lookup from volume indices ixyz to the image volume :param ixyz: [n, 3] array of indices in the mlapdv order :return: n array of flat indices """ idims = np.split(ixyz[..., self.xyz2dims], [1, 2], axis=-1) inds = np.ravel_multi_index(idims, self.bc.nxyz[self.xyz2dims]) return inds.squeeze() def _lookup(self, xyz): """ Performs a 3D lookup from real world coordinates to the flat indices in the volume defined in the BrainCoordinates object :param xyz: [n, 3] array of coordinates :return: n array of flat indices """ return self._lookup_inds(self.bc.xyz2i(xyz))
[docs] def get_labels(self, xyz, mapping='Allen'): """ Performs a 3D lookup from real world coordinates to the volume labels and return the regions ids according to the mapping :param xyz: [n, 3] array of coordinates :param mapping: brain region mapping (defaults to original Allen mapping) :return: n array of region ids """ regions_indices = self._get_mapping(mapping=mapping)[self.label.flat[self._lookup(xyz)]] return self.regions.id[regions_indices]
def _get_mapping(self, mapping='Allen'): """ Safe way to get mappings if nothing defined in regions. A mapping transforms from the full allen brain Atlas ids to the remapped ids new_ids = ids[mapping] """ if hasattr(self.regions, 'mappings'): return self.regions.mappings[mapping] else: return np.arange(self.regions.id.size) def _label2rgb(self, imlabel): """ Converts a slice from the label volume to its RGB equivalent for display :param imlabel: 2D np-array containing label ids (slice of the label volume) :return: 3D np-array of the slice uint8 rgb values """ if getattr(self.regions, 'rgb', None) is None: return self.regions.id[imlabel] else: # if the regions exist and have the rgb attribute, do the rgb lookup return self.regions.rgb[imlabel]
[docs] def tilted_slice(self, xyz, axis, volume='image'): """ From line coordinates, extracts the tilted plane containing the line from the 3D volume :param xyz: np.array: points defining a probe trajectory in 3D space (xyz triplets) if more than 2 points are provided will take the best fit :param axis: 0: along ml = sagittal-slice 1: along ap = coronal-slice 2: along dv = horizontal-slice :param volume: 'image' or 'annotation' :return: np.array, abscissa extent (width), ordinate extent (height), squeezed axis extent (depth) """ if axis == 0: # sagittal slice (squeeze/take along ml-axis) wdim, hdim, ddim = (1, 2, 0) elif axis == 1: # coronal slice (squeeze/take along ap-axis) wdim, hdim, ddim = (0, 2, 1) elif axis == 2: # horizontal slice (squeeze/take along dv-axis) wdim, hdim, ddim = (0, 1, 2) # get the best fit and find exit points of the volume along squeezed axis trj = Trajectory.fit(xyz) sub_volume = trj._eval(self.bc.lim(axis=hdim), axis=hdim) sub_volume[:, wdim] = self.bc.lim(axis=wdim) sub_volume_i = self.bc.xyz2i(sub_volume) tile_shape = np.array([np.diff(sub_volume_i[:, hdim])[0] + 1, self.bc.nxyz[wdim]]) # get indices along each dimension indx = np.arange(tile_shape[1]) indy = np.arange(tile_shape[0]) inds = np.linspace(*sub_volume_i[:, ddim], tile_shape[0]) # compute the slice indices and output the slice _, INDS = np.meshgrid(indx, np.int64(np.around(inds))) INDX, INDY = np.meshgrid(indx, indy) indsl = [[INDX, INDY, INDS][i] for i in np.argsort([wdim, hdim, ddim])[self.xyz2dims]] if isinstance(volume, np.ndarray): tslice = volume[indsl[0], indsl[1], indsl[2]] elif volume.lower() == 'annotation': tslice = self._label2rgb(self.label[indsl[0], indsl[1], indsl[2]]) elif volume.lower() == 'image': tslice = self.image[indsl[0], indsl[1], indsl[2]] elif volume.lower() == 'surface': tslice = self.surface[indsl[0], indsl[1], indsl[2]] # get extents with correct convention NB: matplotlib flips the y-axis on imshow ! width = np.sort(sub_volume[:, wdim])[np.argsort(self.bc.lim(axis=wdim))] height = np.flipud(np.sort(sub_volume[:, hdim])[np.argsort(self.bc.lim(axis=hdim))]) depth = np.flipud(np.sort(sub_volume[:, ddim])[np.argsort(self.bc.lim(axis=ddim))]) return tslice, width, height, depth
[docs] def plot_tilted_slice(self, xyz, axis, volume='image', cmap=None, ax=None, **kwargs): """ From line coordinates, extracts the tilted plane containing the line from the 3D volume :param xyz: np.array: points defining a probe trajectory in 3D space (xyz triplets) if more than 2 points are provided will take the best fit :param axis: 0: along ml = sagittal-slice 1: along ap = coronal-slice 2: along dv = horizontal-slice :param volume: 'image' or 'annotation' :return: matplotlib axis """ if axis == 0: axis_labels = np.array(['ap (um)', 'dv (um)', 'ml (um)']) elif axis == 1: axis_labels = np.array(['ml (um)', 'dv (um)', 'ap (um)']) elif axis == 2: axis_labels = np.array(['ml (um)', 'ap (um)', 'dv (um)']) tslice, width, height, depth = self.tilted_slice(xyz, axis, volume=volume) width = width * 1e6 height = height * 1e6 depth = depth * 1e6 if not ax: plt.figure() ax = plt.gca() ax.axis('equal') if not cmap: cmap = plt.get_cmap('bone') # get the transfer function from y-axis to squeezed axis for second axe ab = np.linalg.solve(np.c_[height, height * 0 + 1], depth) height * ab[0] + ab[1] ax.imshow(tslice, extent=np.r_[width, height], cmap=cmap, **kwargs) sec_ax = ax.secondary_yaxis('right', functions=( lambda x: x * ab[0] + ab[1], lambda y: (y - ab[1]) / ab[0])) ax.set_xlabel(axis_labels[0]) ax.set_ylabel(axis_labels[1]) sec_ax.set_ylabel(axis_labels[2]) return ax
@staticmethod def _plot_slice(im, extent, ax=None, cmap=None, **kwargs): if not ax: ax = plt.gca() ax.axis('equal') if not cmap: cmap = plt.get_cmap('bone') ax.imshow(im, extent=extent, cmap=cmap, **kwargs) return ax
[docs] def extent(self, axis): """ :param axis: direction along which the volume is stacked: (2 = z for horizontal slice) (1 = y for coronal slice) (0 = x for sagittal slice) :return: """ if axis == 0: extent = np.r_[self.bc.ylim, np.flip(self.bc.zlim)] * 1e6 elif axis == 1: extent = np.r_[self.bc.xlim, np.flip(self.bc.zlim)] * 1e6 elif axis == 2: extent = np.r_[self.bc.xlim, np.flip(self.bc.ylim)] * 1e6 return extent
[docs] def slice(self, coordinate, axis, volume='image', mode='raise', region_values=None, mapping="Allen", bc=None): """ :param coordinate: float :param axis: xyz convention: 0 for ml, 1 for ap, 2 for dv - 0: sagittal slice (along ml axis) - 1: coronal slice (along ap axis) - 2: horizontal slice (along dv axis) :param volume: 'image' or 'annotation' :param mode: error mode for out of bounds coordinates - 'raise' raise an error - 'clip' gets the first or last index :param region_values :return: 2d array or 3d RGB numpy int8 array """ index = self.bc.xyz2i(np.array([coordinate] * 3))[axis] # np.take is 50 thousand times slower than straight slicing ! def _take(vol, ind, axis): if mode == 'clip': ind = np.minimum(np.maximum(ind, 0), vol.shape[axis] - 1) if axis == 0: return vol[ind, :, :] elif axis == 1: return vol[:, ind, :] elif axis == 2: return vol[:, :, ind] def _take_remap(vol, ind, axis, mapping): # For the labels, remap the regions indices according to the mapping return self._get_mapping(mapping=mapping)[_take(vol, ind, axis)] if isinstance(volume, np.ndarray): return _take(volume, index, axis=self.xyz2dims[axis]) # add annotation_ids # add annotation_indices # rename annoatation_rgb ? elif volume in 'annotation': iregion = _take_remap(self.label, index, self.xyz2dims[axis], mapping) return self._label2rgb(iregion) elif volume == 'value': return region_values[_take_remap(self.label, index, self.xyz2dims[axis], mapping)] elif volume == 'image': return _take(self.image, index, axis=self.xyz2dims[axis]) elif volume in ['surface', 'edges']: return _take(self.surface, index, axis=self.xyz2dims[axis]) elif volume == 'volume': if bc is not None: index = bc.xyz2i(np.array([coordinate] * 3))[axis] return _take(region_values, index, axis=self.xyz2dims[axis])
[docs] def plot_cslice(self, ap_coordinate, volume='image', mapping='Allen', **kwargs): """ Imshow a coronal slice :param: ap_coordinate (m) :param volume: 'image' or 'annotation' :return: ax """ cslice = self.slice(ap_coordinate, axis=1, volume=volume, mapping=mapping) return self._plot_slice(cslice.T, extent=self.extent(axis=1), **kwargs)
[docs] def plot_hslice(self, dv_coordinate, volume='image', mapping='Allen', **kwargs): """ Imshow a horizontal slice :param: dv_coordinate (m) :param volume: 'image' or 'annotation' :return: ax """ hslice = self.slice(dv_coordinate, axis=2, volume=volume, mapping=mapping) return self._plot_slice(hslice, extent=self.extent(axis=2), **kwargs)
[docs] def plot_sslice(self, ml_coordinate, volume='image', mapping='Allen', **kwargs): """ Imshow a sagittal slice :param: ml_coordinate (m) :param volume: 'image' or 'annotation' :return: ax """ sslice = self.slice(ml_coordinate, axis=0, volume=volume, mapping=mapping) return self._plot_slice(np.swapaxes(sslice, 0, 1), extent=self.extent(axis=0), **kwargs)
[docs] def plot_top(self, ax=None): ix, iy = np.meshgrid(np.arange(self.bc.nx), np.arange(self.bc.ny)) iz = self.bc.z2i(self.top) inds = self._lookup_inds(np.stack((ix, iy, iz), axis=-1)) if not ax: ax = plt.gca() ax.axis('equal') ax.imshow(self._label2rgb(self.label.flat[inds]), extent=self.extent(axis=2), origin='upper') return ax
[docs]@dataclass class Trajectory: """ 3D Trajectory (usually for a linear probe). Minimally defined by a vector and a point. instantiate from a best fit from a n by 3 array containing xyz coordinates: trj = Trajectory.fit(xyz) """ vector: np.ndarray point: np.ndarray
[docs] @staticmethod def fit(xyz): """ fits a line to a 3D cloud of points, returns a Trajectory object :param xyz: n by 3 numpy array containing cloud of points :returns: a Trajectory object """ xyz_mean = np.mean(xyz, axis=0) return Trajectory(vector=np.linalg.svd(xyz - xyz_mean)[2][0], point=xyz_mean)
[docs] def eval_x(self, x): """ given an array of x coordinates, returns the xyz array of coordinates along the insertion :param x: n by 1 or numpy array containing x-coordinates :return: n by 3 numpy array containing xyz-coordinates """ return self._eval(x, axis=0)
[docs] def eval_y(self, y): """ given an array of y coordinates, returns the xyz array of coordinates along the insertion :param y: n by 1 or numpy array containing y-coordinates :return: n by 3 numpy array containing xyz-coordinates """ return self._eval(y, axis=1)
[docs] def eval_z(self, z): """ given an array of z coordinates, returns the xyz array of coordinates along the insertion :param z: n by 1 or numpy array containing z-coordinates :return: n by 3 numpy array containing xyz-coordinates """ return self._eval(z, axis=2)
[docs] def project(self, point): """ projects a point onto the trajectory line :param point: np.array(x, y, z) coordinates :return: """ # https://mathworld.wolfram.com/Point-LineDistance3-Dimensional.html if point.ndim == 1: return self.project(point[np.newaxis])[0] return (self.point + np.dot(point[:, np.newaxis] - self.point, self.vector) / np.dot(self.vector, self.vector) * self.vector)
[docs] def mindist(self, xyz, bounds=None): """ Computes the minimum distance to the trajectory line for one or a set of points. If bounds are provided, computes the minimum distance to the segment instead of an infinite line. :param xyz: [..., 3] :param bounds: defaults to None. np.array [2, 3]: segment boundaries, inf line if None :return: minimum distance [...] """ proj = self.project(xyz) d = np.sqrt(np.sum((proj - xyz) ** 2, axis=-1)) if bounds is not None: # project the boundaries and the points along the traj b = np.dot(bounds, self.vector) ob = np.argsort(b) p = np.dot(xyz[:, np.newaxis], self.vector).squeeze() # for points below and above boundaries, compute cartesian distance to the boundary imin = p < np.min(b) d[imin] = np.sqrt(np.sum((xyz[imin, :] - bounds[ob[0], :]) ** 2, axis=-1)) imax = p > np.max(b) d[imax] = np.sqrt(np.sum((xyz[imax, :] - bounds[ob[1], :]) ** 2, axis=-1)) return d
def _eval(self, c, axis): # uses symmetric form of 3d line equation to get xyz coordinates given one coordinate if not isinstance(c, np.ndarray): c = np.array(c) while c.ndim < 2: c = c[..., np.newaxis] # there are cases where it's impossible to project if a line is // to the axis if self.vector[axis] == 0: return np.nan * np.zeros((c.shape[0], 3)) else: return (c - self.point[axis]) * self.vector / self.vector[axis] + self.point
[docs] def exit_points(self, bc): """ Given a Trajectory and a BrainCoordinates object, computes the intersection of the trajectory with the brain coordinates bounding box :param bc: BrainCoordinate objects :return: np.ndarray 2 y 3 corresponding to exit points xyz coordinates """ bounds = np.c_[bc.xlim, bc.ylim, bc.zlim] epoints = np.r_[self.eval_x(bc.xlim), self.eval_y(bc.ylim), self.eval_z(bc.zlim)] epoints = epoints[~np.all(np.isnan(epoints), axis=1)] ind = np.all(np.bitwise_and(bounds[0, :] <= epoints, epoints <= bounds[1, :]), axis=1) return epoints[ind, :]
[docs]@dataclass class Insertion: """ Defines an ephys probe insertion in 3D coordinate. IBL conventions. To instantiate, use the static methods: Insertion.from_track Insertion.from_dict """ x: float y: float z: float phi: float theta: float depth: float label: str = '' beta: float = 0
[docs] @staticmethod def from_track(xyzs, brain_atlas=None): """ :param brain_atlas: None. If provided, disregards the z coordinate and locks the insertion point to the z of the brain surface :return: Trajectory object """ assert brain_atlas, 'Input argument brain_atlas must be defined' traj = Trajectory.fit(xyzs) # project the deepest point into the vector to get the tip coordinate tip = traj.project(xyzs[np.argmin(xyzs[:, 2]), :]) # get intersection with the brain surface as an entry point entry = Insertion.get_brain_entry(traj, brain_atlas) # convert to spherical system to store the insertion depth, theta, phi = cart2sph(*(entry - tip)) insertion_dict = {'x': entry[0], 'y': entry[1], 'z': entry[2], 'phi': phi, 'theta': theta, 'depth': depth} return Insertion(**insertion_dict)
[docs] @staticmethod def from_dict(d, brain_atlas=None): """ Constructs an Insertion object from the json information stored in probes.description file :param trj: dictionary containing at least the following keys, in um { 'x': 544.0, 'y': 1285.0, 'z': 0.0, 'phi': 0.0, 'theta': 5.0, 'depth': 4501.0 } :param brain_atlas: None. If provided, disregards the z coordinate and locks the insertion point to the z of the brain surface :return: Trajectory object """ z = d['z'] / 1e6 if brain_atlas: iy = brain_atlas.bc.y2i(d['y'] / 1e6) ix = brain_atlas.bc.x2i(d['x'] / 1e6) z = brain_atlas.top[iy, ix] return Insertion(x=d['x'] / 1e6, y=d['y'] / 1e6, z=z, phi=d['phi'], theta=d['theta'], depth=d['depth'] / 1e6, beta=d.get('beta', 0), label=d.get('label', ''))
@property def trajectory(self): """ Gets the trajectory object matching insertion coordinates :return: atlas.Trajectory """ return Trajectory.fit(self.xyz) @property def xyz(self): return np.c_[self.entry, self.tip].transpose() @property def entry(self): return np.array((self.x, self.y, self.z)) @property def tip(self): return sph2cart(- self.depth, self.theta, self.phi) + np.array((self.x, self.y, self.z)) @staticmethod def _get_surface_intersection(traj, brain_atlas, surface='top'): distance = traj.mindist(brain_atlas.srf_xyz) dist_sort = np.argsort(distance) # In some cases the nearest two intersection points are not the top and bottom of brain # So we find all intersection points that fall within one voxel and take the one with # highest dV to be entry and lowest dV to be exit idx_lim = np.sum(distance[dist_sort] * 1e6 < brain_atlas.res_um) dist_lim = dist_sort[0:idx_lim] z_val = brain_atlas.srf_xyz[dist_lim, 2] if surface == 'top': ma = np.argmax(z_val) _xyz = brain_atlas.srf_xyz[dist_lim[ma], :] _ixyz = brain_atlas.bc.xyz2i(_xyz) _ixyz[brain_atlas.xyz2dims[2]] += 1 elif surface == 'bottom': ma = np.argmin(z_val) _xyz = brain_atlas.srf_xyz[dist_lim[ma], :] _ixyz = brain_atlas.bc.xyz2i(_xyz) xyz = brain_atlas.bc.i2xyz(_ixyz.astype(float)) return xyz
[docs] @staticmethod def get_brain_exit(traj, brain_atlas): """ Given a Trajectory and a BrainAtlas object, computes the brain exit coordinate as the intersection of the trajectory and the brain surface (brain_atlas.surface) :param brain_atlas: :return: 3 element array x,y,z """ # Find point where trajectory intersects with bottom of brain return Insertion._get_surface_intersection(traj, brain_atlas, surface='bottom')
[docs] @staticmethod def get_brain_entry(traj, brain_atlas): """ Given a Trajectory and a BrainAtlas object, computes the brain entry coordinate as the intersection of the trajectory and the brain surface (brain_atlas.surface) :param brain_atlas: :return: 3 element array x,y,z """ # Find point where trajectory intersects with top of brain return Insertion._get_surface_intersection(traj, brain_atlas, surface='top')
[docs]class AllenAtlas(BrainAtlas): """ Instantiates an atlas.BrainAtlas corresponding to the Allen CCF at the given resolution using the IBL Bregma and coordinate system """ def __init__(self, res_um=25, scaling=np.array([1, 1, 1]), mock=False, hist_path=None): """ :param res_um: 10, 25 or 50 um :param scaling: scale factor along ml, ap, dv for squeeze and stretch ([1, 1, 1]) :param mock: for testing purpose :param hist_path :return: atlas.BrainAtlas """ par = one.params.get(silent=True) FLAT_IRON_ATLAS_REL_PATH = PurePosixPath('histology', 'ATLAS', 'Needles', 'Allen') LUT_VERSION = "v01" # version 01 is the lateralized version regions = BrainRegions() xyz2dims = np.array([1, 0, 2]) # this is the c-contiguous ordering dims2xyz = np.array([1, 0, 2]) # we use Bregma as the origin self.res_um = res_um ibregma = (ALLEN_CCF_LANDMARKS_MLAPDV_UM['bregma'] / self.res_um) dxyz = self.res_um * 1e-6 * np.array([1, -1, -1]) * scaling if mock: image, label = [np.zeros((528, 456, 320), dtype=np.int16) for _ in range(2)] label[:, :, 100:105] = 1327 # lookup index for retina, id 304325711 (no id 1327) else: path_atlas = Path(par.CACHE_DIR).joinpath(FLAT_IRON_ATLAS_REL_PATH) file_image = hist_path or path_atlas.joinpath(f'average_template_{res_um}.nrrd') # get the image volume if not file_image.exists(): _download_atlas_allen(file_image, FLAT_IRON_ATLAS_REL_PATH, par) # get the remapped label volume file_label = path_atlas.joinpath(f'annotation_{res_um}.nrrd') if not file_label.exists(): _download_atlas_allen(file_label, FLAT_IRON_ATLAS_REL_PATH, par) file_label_remap = path_atlas.joinpath(f'annotation_{res_um}_lut_{LUT_VERSION}.npz') if not file_label_remap.exists(): label = self._read_volume(file_label) _logger.info("computing brain atlas annotations lookup table") # lateralize atlas: for this the regions of the left hemisphere have primary # keys opposite to to the normal ones lateral = np.zeros(label.shape[xyz2dims[0]]) lateral[int(np.floor(ibregma[0]))] = 1 lateral = np.sign(np.cumsum(lateral)[np.newaxis, :, np.newaxis] - 0.5) label = label * lateral _, im = ismember(label, regions.id) label = np.reshape(im.astype(np.uint16), label.shape) _logger.info(f"saving {file_label_remap} ...") np.savez_compressed(file_label_remap, label) # loads the files label = self._read_volume(file_label_remap) image = self._read_volume(file_image) super().__init__(image, label, dxyz, regions, ibregma, dims2xyz=dims2xyz, xyz2dims=xyz2dims) @staticmethod def _read_volume(file_volume): if file_volume.suffix == '.nrrd': volume, _ = nrrd.read(file_volume, index_order='C') # ml, dv, ap # we want the coronal slice to be the most contiguous volume = np.transpose(volume, (2, 0, 1)) # image[iap, iml, idv] elif file_volume.suffix == '.npz': volume = np.load(file_volume)['arr_0'] return volume
[docs] def xyz2ccf(self, xyz, ccf_order='mlapdv'): """ Converts coordinates to the CCF coordinates, which is assumed to be the cube indices times the spacing. :param xyz: mlapdv coordinates in um, origin Bregma :param ccf_order: order that you want values returned 'mlapdv' (ibl) or 'apdvml' (Allen mcc vertices) :return: coordinates in CCF space um, origin is the front left top corner of the data volume, order determined by ccf_order """ ordre = self._ccf_order(ccf_order) ccf = self.bc.xyz2i(xyz, round=False) * float(self.res_um) return ccf[..., ordre]
[docs] def ccf2xyz(self, ccf, ccf_order='mlapdv'): """ Converts coordinates from the CCF coordinates, which is assumed to be the cube indices times the spacing. :param ccf coordinates in CCF space in um, origin is the front left top corner of the data volume :param ccf_order: order of ccf coordinates given 'mlapdv' (ibl) or 'apdvml' (Allen mcc vertices) :return: xyz: mlapdv coordinates in um, origin Bregma """ ordre = self._ccf_order(ccf_order, reverse=True) return self.bc.i2xyz((ccf[..., ordre] / float(self.res_um)))
@staticmethod def _ccf_order(ccf_order, reverse=False): """ Returns the mapping to go from CCF coordinates order to the brain atlas xyz :param ccf_order: 'mlapdv' or 'apdvml' :param reverse: defaults to False. If False, returns from CCF to brain atlas If True, returns from brain atlas to CCF :return: """ if ccf_order == 'mlapdv': return [0, 1, 2] elif ccf_order == 'apdvml': if reverse: return [2, 0, 1] else: return [1, 2, 0] else: ValueError("ccf_order needs to be either 'mlapdv' or 'apdvml'")
[docs]def NeedlesAtlas(*args, **kwargs): """ Instantiates an atlas.BrainAtlas corresponding to the Allen CCF at the given resolution using the IBL Bregma and coordinate system. The Needles atlas defines a stretch along AP axis and a sqeeze along the DV axis. :param res_um: 10, 25 or 50 um :return: atlas.BrainAtlas """ DV_SCALE = 0.952 # multiplicative factor on DV dimension, determined from MRI->CCF transform AP_SCALE = 1.087 # multiplicative factor on AP dimension kwargs['scaling'] = np.array([1, AP_SCALE, DV_SCALE]) return AllenAtlas(*args, **kwargs)
def _download_atlas_allen(file_image, FLAT_IRON_ATLAS_REL_PATH, par): """ © 2015 Allen Institute for Brain Science. Allen Mouse Brain Atlas (2015) with region annotations (2017). Available from: http://download.alleninstitute.org/informatics-archive/current-release/ mouse_ccf/annotation/ See Allen Mouse Common Coordinate Framework Technical White Paper for details http://help.brain-map.org/download/attachments/8323525/ Mouse_Common_Coordinate_Framework.pdf?version=3&modificationDate=1508178848279&api=v2 """ file_image.parent.mkdir(exist_ok=True, parents=True) template_url = ('http://download.alleninstitute.org/informatics-archive/' 'current-release/mouse_ccf/average_template') annotation_url = ('http://download.alleninstitute.org/informatics-archive/' 'current-release/mouse_ccf/annotation/ccf_2017') if file_image.name.split('_')[0] == 'average': url = template_url + '/' + file_image.name elif file_image.name.split('_')[0] == 'annotation': url = annotation_url + '/' + file_image.name else: raise ValueError('Unrecognized file image') cache_dir = Path(par.CACHE_DIR).joinpath(FLAT_IRON_ATLAS_REL_PATH) return http_download_file(url, cache_dir=cache_dir)