#!/usr/bin/env python2 # -*- coding: utf-8 -*- """ TubeMap for axons ======= This script is the main pipeline to generate annotated graphs from axon tracing data lightsheet data. It uses TubeMap [Kirst2020]_ and TrailMap [Friedmann2020]_. See the :ref:`TubeMap tutorial ` for a tutorial and usage. .. image:: ../Static/cell_abstract_2020.jpg :target: https://doi.org/10.1016/j.cell.2016.05.007 :width: 300 References ---------- .. [Kirst2020] `Mapping the Fine-Scale Organization and Plasticity of the Brain Vasculature. Kirst, C., Skriabine, S., Vieites-Prado, A., Topilko, T., Bertin, P., Gerschenfeld, G., Verny, F., Topilko, P., Michalski, N., Tessier-Lavigne, M. and Renier, N., Cell, 180(4):780-795 `_ .. [Friedmann2020] Mapping mesoscale axonal projections in the mouse brain using a 3D convolutional network. Drew Friedmann, Albert Pun, Eliza L Adams, Jan H Lui, Justus M Kebschull, Sophie M Grutzner, Caitlin Castagnola, Marc Tessier-Lavigne, Liqun Luo Proceedings of the National Academy of Sciences 2020 https://dx.doi.org/10.1073/pnas.1918465117 """ __author__ = 'Nicolas Renier' __webpage__ = 'http://idisco.info' __download__ = 'http://www.github.com/ChristophKirst/ClearMap2' if __name__ == '__main__': ### Initialization ############################################################################### #%% Initialize workspace from ClearMap.Environment import * #analysis:ignore from TrailMap.inference import segment_brain from TrailMap.models import model #directories and files directory = '/YOURPATHHERE/BRAINNUMBER' #main sample folder projections_directory = '/YOURPATHHERE/BRAINNUMBER/IMAGESEQUENCE' #Input image files with regular expression expression_raw = 'IMAGESEQUENCE/Z.tif' raw = io.join(directory,expression_raw) expression_auto = 'AUTOIMAGES/Z.ome.tif' #imqge sequence for autofluorescence with regular expression auto = io.join(directory, expression_auto) resources_directory = settings.resources_path ws = wsp.Workspace('TubeMap', directory=directory); ws.update(raw=expression_raw, autofluorescence=expression_auto) ws.info() #%% Initialize alignment #init atals and reference files annotation_file, reference_file, distance_file=ano.prepare_annotation_files( slicing=(slice(None),slice(None),slice(None)), orientation=(-3,2,1), overwrite=False, verbose=True); #alignment parameter files align_channels_affine_file = io.join(resources_directory, 'Alignment/align_affine.txt') align_reference_affine_file = io.join(resources_directory, 'Alignment/align_affine.txt') align_reference_bspline_file = io.join(resources_directory, 'Alignment/align_bspline.txt') #%%############################################################################ ### Resampling and atlas alignment ############################################################################### #%% Resample resample_parameter = { "source_resolution" : (5.9,5.9,3), "sink_resolution" : (25,25,25), "processes" : None, "verbose" : True, }; io.delete_file(ws.filename('resampled')); res.resample(raw, sink=ws.filename('resampled'), **resample_parameter) #%% Resample autofluorescence resample_parameter_auto = { "source_resolution" : (5.9,5.9,6), "sink_resolution" : (25,25,25), "processes" : None, "verbose" : True, }; io.delete_file(ws.filename('resampled', postfix='autofluorescence')); res.resample(auto, sink=ws.filename('resampled', postfix='autofluorescence'), **resample_parameter_auto) #p3d.plot([ws.filename('resampled'), ws.filename('resampled', postfix='autofluorescence')]) #%% Aignment - resampled to autofluorescence # align the two channels align_channels_parameter = { #moving and reference images "moving_image" : ws.filename('resampled', postfix='autofluorescence'), "fixed_image" : ws.filename('resampled'), #elastix parameter files for alignment "affine_parameter_file" : align_channels_affine_file, "bspline_parameter_file" : None, #directory of the alig'/home/nicolas.renier/Documents/ClearMap_Ressources/Par0000affine.txt',nment result "result_directory" : ws.filename('resampled_to_auto') }; elx.align(**align_channels_parameter); #%% Alignment - autoflourescence to reference # align autofluorescence to reference align_reference_parameter = { #moving and reference images "moving_image" : reference_file, "fixed_image" : ws.filename('resampled', postfix='autofluorescence'), #elastix parameter files for alignment "affine_parameter_file" : align_reference_affine_file, "bspline_parameter_file" : align_reference_bspline_file, #directory of the alignment result "result_directory" : ws.filename('auto_to_reference') }; elx.align(**align_reference_parameter); #%% RUN TRAILMAP MODELS trailmap_model = model.get_net() trailmap_model.load_weights('/modelpath/model.hdf5') segment_brain(projections_directory, directory, trailmap_model) #%%############################################################################ ### Graph construction and measurements ############################################################################### modelbinary = io.read('/PATH TO MODEL RESULT/model.tif') #the output of the model is converted to a single 8bit thresholded tif file with fiji boolean = modelbinary > 0 #conversion of the thresholded tif file to a binary #%% Skeletonize skeleton = ws.filename('skeleton') skl.skeletonize(boolean, sink=skeleton, delete_border=True, verbose=True); #%% Graph from skeleton graph_raw = gp.graph_from_skeleton(ws.filename('skeleton'), verbose=True) #graph_raw.save(ws.filename('graph', postfix='raw')) #%% Measure radii coordinates = graph_raw.vertex_coordinates(); radii, indices = mr.measure_radius(boolean, coordinates, value=0, fraction=None, max_radius=150, # value=None, fraction=0.8, max_radius=150, return_indices=True, default=-1, verbose=True); graph_raw.set_vertex_radii(radii) #%% Save raw graph graph_raw.save(ws.filename('graph', postfix='raw')) #graph_raw = grp.load(ws.filename('graph', postfix='raw')) #%%############################################################################ ### Graph cleaning and reduction ############################################################################### #%% Graph cleaning graph_cleaned = gp.clean_graph(graph_raw, vertex_mappings = {'coordinates' : gp.mean_vertex_coordinates, 'radii' : np.max, 'artery_binary' : np.max, 'artery_raw' : np.max}, verbose=True) #%% Save cleaned graph graph_cleaned.save(ws.filename('graph', postfix='cleaned')) #graph_cleaned = grp.load(ws.filename('graph', postfix='cleaned')); #%% Graph reduction def vote(expression): return np.sum(expression) >= len(expression) / 1.5; graph_reduced = gp.reduce_graph(graph_cleaned, edge_length=True, edge_to_edge_mappings = {'length' : np.sum}, vertex_to_edge_mappings={'artery_binary' : vote, 'artery_raw' : np.max, 'radii' : np.max}, edge_geometry_vertex_properties=['coordinates', 'radii', 'artery_binary', 'artery_raw'], edge_geometry_edge_properties=None, return_maps=False, verbose=True) #%% Save reduced graph graph_reduced.save(ws.filename('graph', postfix='reduced')) #graph_reduced = grp.load(ws.filename('graph', postfix='reduced')); #%%############################################################################ ### Atlas registration and annotation ############################################################################### #%% Graph atlas registration def transformation(coordinates): coordinates = res.resample_points( coordinates, sink=None, orientation=None, source_shape=io.shape(ws.filename('skeleton')), sink_shape=io.shape(ws.filename('resampled'))); coordinates = elx.transform_points( coordinates, sink=None, transform_directory=ws.filename('resampled_to_auto'), binary=True, indices=False); coordinates = elx.transform_points( coordinates, sink=None, transform_directory=ws.filename('auto_to_reference'), binary=True, indices=False); return coordinates; graph_reduced.transform_properties(transformation=transformation, vertex_properties = {'coordinates' : 'coordinates_atlas'}, edge_geometry_properties = {'coordinates' : 'coordinates_atlas'}, verbose=True); def scaling(radii): resample_factor = res.resample_factor( source_shape=io.shape(ws.filename('skeleton')), sink_shape=io.shape(ws.filename('resampled'))) return radii * np.mean(resample_factor); graph_reduced.transform_properties(transformation=scaling, vertex_properties = {'radii' : 'radii_atlas'}, edge_properties = {'radii' : 'radii_atlas'}, edge_geometry_properties = {'radii' : 'radii_atlas'}) #%% Graph atlas annotation ano.set_annotation_file(annotation_file) def annotation(coordinates): label = ano.label_points(coordinates, key='order'); return label; graph_reduced.annotate_properties(annotation, vertex_properties = {'coordinates_atlas' : 'annotation'}, edge_geometry_properties = {'coordinates_atlas' : 'annotation'}); #%% Save annotated graph graph = graph_reduced.copy() graph.save(ws.filename('graph', postfix='annotated')) graph = grp.load(ws.filename('graph', postfix='annotated')); #%% *2 in x and y + plot: correction of the anisotropic coordinates for visualization only coordinates=graph.vertex_property('coordinates') coordinates[:, 0]=coordinates[:, 0]*1.97 coordinates[:, 1]=coordinates[:, 1]*1.97 graph.set_vertex_property('coordinates', coordinates) #%% plot #3D visualization of the results label = graph.vertex_annotation(); label_leveled = ano.convert_label(label, key='order', value='order', level=1) vertex_filter = label_leveled == 1; graph = graph.sub_graph(vertex_filter=vertex_filter); vertex_colors = ano.convert_label(graph.vertex_annotation(), key='order', value='rgba'); p = p3d.plot_graph_line(graph, color=vertex_colors) # p = p3d.plot_graph_mesh(graph, default_radius=0.15, vertex_colors=vertex_colors, n_tube_points=3) #%% Voxelization #Voxel heatmap of axon densities voxelize_branch_parameter = { "method": 'sphere', "radius": (15, 15, 15), "weights": None, "shape": io.shape(reference_file), "verbose": True }; vertices = graph.vertex_property('coordinates_atlas'); branch_density = vox.voxelize(vertices, sink=ws.filename('density', postfix='branches'), dtype='float32', **voxelize_branch_parameter);