Package: brainglobe-space
brainglobe-spaceAnatomical space conventions made easy.Working with anatomical images, one often encounters the problem of matching the orientation of stacks with different conventions about axes orientation and order. Moreover, when multiple swaps and flips are involved, it can be annoying to map the same transformations to volumes and points (e.g., coordinates or meshes).brainglobe-spaceprovides a neat way of defining an anatomical space, and of operating stacks and point transformations between spaces.If you usebrainglobe-spacefor your analyses, please cite itsZenodo DOIhttps://zenodo.org/record/4552537#.YDAFzi1Q2Rs!InstallationYou can installbrainglobe-spacewith:pipinstallbrainglobe-spaceUsageTo define a new anatomical space, it is sufficient to give the directions of the stack origin position:source_origin=("Left","Superior","Anterior")target_origin=("Inferior","Posterior","Right")A stack can be then easily transformed from the source to the target space:importbrainglobe_spaceasbgimportnumpyasnpstack=np.random.rand(3,2,4)mapped_stack=bg.map_stack_to(source_origin,target_origin,stack)The transformation is handled only with numpy index operations;i.e., no complex
image affine transformations are applied. This is often useful as the preparatory step for starting any kind of image registration.A shortened syntax can be used to define a space using initials of the origin directions:mapped_stack=bg.map_stack_to("lsa","ipr",stack)NOTEWhen you work with a stack, the origin is the upper left corner when you show the first elementstack[0, :, :]with matplotlib or when you open the stack with ImageJ. First dimension is the one that you are slicing, the second the height of the image, and the third the width of the image.TheAnatomicalSpaceclassSometimes, together with the stack we have to move a set of points (cell coordinates, meshes, etc.). This introduces the additional complexity of keeping track, together with the axes swaps and flips, of the change of the origin offset.To handle this situation, we can define a source space using theAnatomicalSpaceclass, specifying also the stack shape:stack=np.random.rand(3,2,4)# a stack in source spaceannotations=np.array([[0,0,0],[2,1,3]])# related point annotationssource_space=bg.AnatomicalSpace(target_origin,stack.shape)mapped_stack=source_space.map_stack_to("ipr",stack)# transform the stackmapped_annotations=source_space.map_points_to("ipr",annotations)# transform the pointsThe points are transformed through the generation of a transformation matrix.
Finally, if we want to log this matrix (e.g., to reconstruct the full transformations sequence of a registration), we can get it:target_space=bg.AnatomicalSpace("ipr",stack.shape)transformation_matrix=source_space.transformation_matrix_to(target_space)# equivalent to:transformation_matrix=source_space.transformation_matrix_to("ipr",stack.shape)The target get always be defined as abg.AnatomicalSpaceobject, or a valid origin specification plus a shape (the shape is required only if axes flips are required).Matching space resolutions and offsetsTheAnatomicalSpaceclass can deal also with stack resampling/padding/cropping. This requires simply specifying values for resolutions and offsets when instantiating aAnatomicalSpaceobject. Once that is done, usingAnatomicalSpace.transformation_matrix_tocreating affine transformation matrices from one space to the other will be a piece of cake!source_space=bgs.AnatomicalSpace("asl",resolution=(2,1,2),offset=(1,0,0))target_space=bgs.AnatomicalSpace("sal",resolution=(1,1,1),offset=(0,0,2))source_space.transformation_matrix_to(target_space)Moreover, we can now use those space objects to resample stacks, and to generate stacks matching a target shape with the correct padding/cropping simply by specifying a target offset:source_space=bgs.AnatomicalSpace("asl",resolution=(2,1,2),offset=(1,0,0))target_space=bgs.AnatomicalSpace("asl",resolution=(1,1,1),shape=(5,4,2))# we need a target shapesource_space.transformation_matrix_to(target_space,stack,to_target_shape=True)Easy iteration over projectionsFinally, another convenient feature of BGSpace is the possibility of iterating easily through the projections of the stack and generate section names and axes labels:sc=bg.AnatomicalSpace("asl")# origin for the stack to be plottedfori,(plane,labels)inenumerate(zip(sc.sections,sc.axis_labels)):axs[i].imshow(stack.mean(i))axs[i].set_title(f"{plane.capitalize()}view")axs[i].set_ylabel(labels[0])axs[i].set_xlabel(labels[1])ContributingContributions are very welcome. Please see thedevelopers guide.