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find_somata_2020_01_13_v2.m
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find_somata_2020_01_13_v2.m
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sample_date = '2020-01-13' ;
tag = 'v2' ;
output_folder_path = sprintf('/groups/mousebrainmicro/mousebrainmicro/cluster/Reconstructions/%s/soma-predictions-%s', sample_date, tag) ;
rendered_folder_path = sprintf('/nrs/mouselight/SAMPLES/%s', sample_date) ;
do_force_computation = false ;
do_use_bsub = true ;
do_actually_submit = true ;
stdout_file_path_template = fullfile(output_folder_path, 'find-somata-%d-%d-%d.out.txt') ;
bsub_options_template = ['-P mouselight -n8 -eo ' stdout_file_path_template ' -oo ' stdout_file_path_template ' -W 59 -J find-somata'] ;
%bsub_options = '-P mouselight -n8 -eo /dev/null -oo /dev/null -W 59 -J find-somata' ;
gfp_channel_index = 0 ;
background_channel_index = 1 ;
zoom_level = 4 ; % The zoom level of the tiles we will analyze
pad_depth_in_um = 50 ; % um
% intensity_threshold = 40000 ;
% minimum_volume = 500 ; % um^3
% maximum_volume = 15000 ; % um^3
% maximum_sqrt_condition_number = 10 ;
intensity_threshold = 0.75 * 2^16 ;
minimum_volume = 500 ; % um^3
%maximum_volume = 15000 ; % um^3
maximum_volume = 25000 ; % um^3
%maximum_sqrt_condition_number = 10 ;
maximum_sqrt_condition_number = 20 ;
parameters = struct('intensity_threshold', {intensity_threshold}, ...
'minimum_volume', {minimum_volume}, ...
'maximum_volume', {maximum_volume}, ...
'maximum_sqrt_condition_number', maximum_sqrt_condition_number) ;
% this_file_path = mfilename('fullpath') ;
% this_folder_path = fileparts(this_file_path) ;
% output_folder_path = fullfile(this_folder_path, sprintf('auto-somata-%s-%s', sample_date, tag)) ;
%[shape_xyz, origin_xyz, spacing_xyz] = load_sample_shape_origin_and_spacing(rendered_folder_path) ;
render_parameters_file_path = fullfile(rendered_folder_path, 'calculated_parameters.jl') ;
render_parameters = read_renderer_calculated_parameters_file(render_parameters_file_path) ;
max_zoom_level = render_parameters.level_step_count ;
chunk_shape_ijk = render_parameters.leaf_shape ; % xyz order, same at all zoom levels, just the chunk count changes
spacing_at_max_zoom_xyz = render_parameters.spacing ;
origin_at_max_zoom_xyz = render_parameters.origin ;
%shape_xyz = 2^max_zoom_level * chunk_shape_ijk ;
spacing_at_zoom_level_0_xyz = 2^max_zoom_level * spacing_at_max_zoom_xyz ;
spacing_at_zoom_level_xyz = spacing_at_zoom_level_0_xyz ./ (2^zoom_level) ;
heckbert_origin_xyz = origin_at_max_zoom_xyz - spacing_at_max_zoom_xyz/2 ; % this origin does not change with the zoom level
origin_at_zoom_level_xyz = heckbert_origin_xyz + spacing_at_zoom_level_xyz/2 ;
stack_shape_ijk = chunk_shape_ijk * 2^zoom_level ; % the shape of the full-brain stack, at zoom level zoom_level
stack_shape_xyz = stack_shape_ijk .* spacing_at_zoom_level_xyz % the shape of the full-brain stack in um, does not change with zoom level
analysis_chunk_shape_ijk = 4*chunk_shape_ijk ;
chunks_per_dimension_ijk = stack_shape_ijk ./ analysis_chunk_shape_ijk ;
if do_force_computation ,
system(sprintf('rm -rf %s', output_folder_path)) ;
end
if ~exist(output_folder_path, 'file') ,
mkdir(output_folder_path) ;
end
chunks_folder_path = fullfile(output_folder_path, 'chunks') ;
if ~exist(chunks_folder_path, 'file') ,
mkdir(chunks_folder_path) ;
end
job_count = prod(chunks_per_dimension_ijk) ;
job_id_array = repmat(-1, [1 job_count]) ;
job_index = 1 ;
for chunk_i = 1 : chunks_per_dimension_ijk(1) ,
for chunk_j = 1 : chunks_per_dimension_ijk(2) ,
for chunk_k = 1 : chunks_per_dimension_ijk(3) ,
somata_mat_file_name = sprintf('somata-for-chunk-%d-%d-%d.mat', chunk_i, chunk_j, chunk_k) ;
somata_mat_file_path = fullfile(chunks_folder_path, somata_mat_file_name) ;
chunk_offset_within_chunks_ijk1 = [chunk_i chunk_j chunk_k]
chunk_offset_within_stack_ijk1 = (chunk_offset_within_chunks_ijk1-1) .* analysis_chunk_shape_ijk + 1 ;
if ~exist(somata_mat_file_path, 'file') ,
if do_use_bsub ,
bsub_options = sprintf(bsub_options_template, chunk_i, chunk_j, chunk_k, chunk_i, chunk_j, chunk_k)
job_id_array(job_index) = ...
bsub(do_actually_submit, ...
bsub_options, ...
@pad_and_find_candidate_somata_then_save, ...
somata_mat_file_path, ...
rendered_folder_path, ...
gfp_channel_index, ...
background_channel_index, ...
zoom_level, ...
origin_at_zoom_level_xyz, ...
spacing_at_zoom_level_xyz, ...
chunk_offset_within_stack_ijk1, ...
analysis_chunk_shape_ijk, ...
pad_depth_in_um, ...
parameters) ;
else
tic_id = tic() ;
pad_and_find_candidate_somata_then_save(somata_mat_file_path, ...
rendered_folder_path, ...
gfp_channel_index, ...
background_channel_index, ...
zoom_level, ...
origin_at_zoom_level_xyz, ...
spacing_at_zoom_level_xyz, ...
chunk_offset_within_stack_ijk1, ...
analysis_chunk_shape_ijk, ...
pad_depth_in_um, ...
parameters) ;
toc(tic_id) ;
end
end
job_index = job_index + 1 ;
end
end
end
% Wait for all those jobs to complete
bwait(job_id_array) ;
%%
% Read in all the .mat files
soma_mat_file_name_template = fullfile(chunks_folder_path, 'somata-for-chunk-*.mat') ;
soma_mat_file_names = simple_dir(soma_mat_file_name_template) ;
somata_file_count = length(soma_mat_file_names) ;
%xyz_from_guess_index = zeros(0,3) ;
%feature_struct_from_guess_index = struct_with_shape_and_fields([0 1], somalike_feature_list()) ;
feature_struct_from_candidate_index = compute_derived_component_features(zeros(0,1)) ;
for i = 1 : somata_file_count ,
somata_mat_file_name = soma_mat_file_names{i} ;
somata_mat_file_path = fullfile(chunks_folder_path, somata_mat_file_name) ;
s = load(somata_mat_file_path) ;
%xyz_from_guess_index = vertcat(xyz_from_guess_index, s.xyz_from_guess_index) ; %#ok<AGROW>
%feature_struct_from_guess_index = vertcat(feature_struct_from_guess_index, s.feature_struct_from_guess_index) ; %#ok<AGROW>
feature_struct_from_candidate_index = vertcat(feature_struct_from_candidate_index, s.feature_struct_from_candidate_index) ; %#ok<AGROW>
end
%guess_count = size(xyz_from_guess_index, 1) ;
candidate_count = length(feature_struct_from_candidate_index) ;
% break out the individual fields
voxel_count_from_candidate_index = [feature_struct_from_candidate_index.voxel_count]' ;
sqrt_condition_number_from_candidate_index = [feature_struct_from_candidate_index.sqrt_condition_number]' ;
max_intensity_from_candidate_index = [feature_struct_from_candidate_index.max_intensity]' ;
max_background_intensity_from_candidate_index = [feature_struct_from_candidate_index.max_background_intensity]' ;
% Get a mip of the whole brain, for visualization
overview_zoom_level = 2 ;
stack_shape_at_overview_zoom_level_ijk = chunk_shape_ijk * 2^overview_zoom_level ;
spacing_at_overview_zoom_level_xyz = spacing_at_zoom_level_0_xyz ./ (2^overview_zoom_level) ;
origin_at_overview_zoom_level_xyz = heckbert_origin_xyz + spacing_at_overview_zoom_level_xyz/2 ;
whole_brain_at_overview_zoom_level_yxz = ...
get_mouselight_rendered_substack(rendered_folder_path, gfp_channel_index, [1 1 1], stack_shape_at_overview_zoom_level_ijk, overview_zoom_level) ;
overview_mip_yx = max(whole_brain_at_overview_zoom_level_yxz, [], 3) ;
mip_origin_xy = origin_at_overview_zoom_level_xyz(1:2) ;
mip_spacing_xy = spacing_at_overview_zoom_level_xyz(1:2) ;
% Load the ground-truth (these are all soma locations)
%[xyz_from_target_index, name_from_target_index] = load_traceable_soma_targets_from_tracers() ;
xyz_from_target_index = zeros(0,3) ;
name_from_target_index = cell(0,1) ;
target_count = size(xyz_from_target_index, 1) ;
% Report the perf of the candidates
do_plot_candidates = false ;
[matching_candidate_index_from_target_index, matching_target_index_from_candidate_index] = ...
print_performace_statistics_and_plot('candidates', ...
xyz_from_target_index, ...
feature_struct_from_candidate_index, ...
heckbert_origin_xyz, ...
stack_shape_xyz, ...
spacing_at_zoom_level_xyz, ...
overview_mip_yx, ...
mip_origin_xy, ...
mip_spacing_xy, ...
[], ...
do_plot_candidates) ;
volume_per_voxel = prod(spacing_at_zoom_level_xyz) ;
minimum_volume_in_voxels = minimum_volume / volume_per_voxel
maximum_volume_in_voxels = maximum_volume / volume_per_voxel
is_guess_from_candidate_index = ...
(minimum_volume_in_voxels < voxel_count_from_candidate_index) & ...
(voxel_count_from_candidate_index < maximum_volume_in_voxels) & ...
sqrt_condition_number_from_candidate_index < maximum_sqrt_condition_number & ...
max_intensity_from_candidate_index > max_background_intensity_from_candidate_index ;
feature_struct_from_guess_index = feature_struct_from_candidate_index(is_guess_from_candidate_index) ;
guess_count = length(feature_struct_from_guess_index) ;
% % Save auto-somata as a .swc file, which can hold a forest, it turns out
% forest_name = sprintf('%s-%s-auto-somata', sample_date, tag) ;
% forest_color = [1 0 0] ;
% swc_file_name = horzcat(forest_name, '.swc') ;
% swc_file_path = fullfile(output_folder_path, swc_file_name) ;
% save_somata_as_single_swc(swc_file_path, xyz_from_guess_index, forest_name, forest_color) ;
% save_somata_as_multiple_swcs(
% output the predictions
centroidoid_xyz_from_guess_index = reshape([feature_struct_from_guess_index(:).centroidoid_xyz], [3 guess_count])' ;
name_template = 'soma-prediction-%d' ;
output_swc_file_name_template = fullfile(output_folder_path, 'swcs', 'soma-prediction-%d.swc') ;
color = [1 0 1] ; % magenta
%save_somata_as_single_swc(output_swc_file_name, centroidoid_xyz_from_guess_index, name, color)
save_somata_as_multiple_swcs(output_swc_file_name_template, centroidoid_xyz_from_guess_index, name_template, color) ;
% f = figure('color', 'w') ;
% a = axes(f, 'YDir', 'reverse') ;
% image(a, 'CData', substack_mip, ...
% 'XData', [padded_substack_origin_xyz(1) padded_substack_far_corner_xyz(1)], ...
% 'YData', [padded_substack_origin_xyz(2) padded_substack_far_corner_xyz(2)], ...
% 'CDataMapping', 'scaled') ;
% colormap(gray(256)) ;
% axis image
%
% hold on ;
% candidate_count = size(candidate_centroid_xyz_from_label,1) ;
% for i = 1 : candidate_count ,
% centroid_xyz = candidate_centroid_xyz_from_label(i,:) ;
% plot(centroid_xyz(1), centroid_xyz(2), '+', 'Color', [0 0.5 1]) ;
% end
% hold off ;
%
% hold on ;
% somata_count = size(putative_somata_xyzs,1) ;
% for i = 1 : somata_count ,
% soma_xyz = putative_somata_xyzs(i,:) ;
% plot(soma_xyz(1), soma_xyz(2), 'r+') ;
% end
% hold off ;
% miss_1_xy = [74096.2049 18331.3971] % location of a candidate centroid that is *not* classified as a soma, but should be
% distance_to_miss_1_xy = sqrt(sum((candidate_centroid_xyz_from_label(:,1:2) - miss_1_xy).^2,2)) ;
% [distance_from_miss_1_to_nearest_candidate_in_xy, label_of_miss_1] = min(distance_to_miss_1_xy)
%
% centroid_xyz_of_miss_1 = candidate_centroid_xyz_from_label(label_of_miss_1, :)
% voxel_count_of_miss_1 = voxel_count_from_label(label_of_miss_1)
% sqrt_condition_number_of_miss_1 = sqrt_condition_number_from_label(label_of_miss_1)
% is_putative_soma_for_miss_1 = is_putative_soma_from_label(label_of_miss_1)
% max_intesity_for_miss_1 = max_intensity_from_label(label_of_miss_1)
% % sqrt condition number is 2.6, voxel_count is 556, which just clears the
% % current minimum (515)
% Report the perf of the guesses
do_plot_candidates = true ;
mip_clim = [11000 2^16-1] ;
matching_guess_index_from_target_index = ...
print_performace_statistics_and_plot('guesses', ...
xyz_from_target_index, ...
feature_struct_from_guess_index, ...
heckbert_origin_xyz, ...
stack_shape_xyz, ...
spacing_at_zoom_level_xyz, ...
overview_mip_yx, ...
mip_origin_xy, ...
mip_spacing_xy, ...
mip_clim, ...
do_plot_candidates) ;
%%
% Considering just the cadidates, plot them in feature space, and
% characterize each as hit/miss/chase/ball. This makes sense b/c we cast a wide net: every target is
% also a candidate.
is_there_a_matched_candidate_from_target_index = isfinite(matching_candidate_index_from_target_index) ;
is_there_a_matched_target_from_candidate_index = isfinite(matching_target_index_from_candidate_index) ;
is_positive_from_candidate_index = is_there_a_matched_target_from_candidate_index ;
is_negative_from_candidate_index = ~is_there_a_matched_target_from_candidate_index ;
positive_count_within_candidates = sum(is_positive_from_candidate_index)
negative_count_within_candidates = sum(is_negative_from_candidate_index)
is_test_positive_from_candidate_index = is_guess_from_candidate_index ;
is_test_negative_from_candidate_index = ~is_guess_from_candidate_index ;
test_positive_count_within_candidates = sum(is_test_positive_from_candidate_index)
test_negative_count_within_candidates = sum(is_test_negative_from_candidate_index)
is_hit_from_candidate_index = is_test_positive_from_candidate_index & is_positive_from_candidate_index ;
is_miss_from_candidate_index = is_test_negative_from_candidate_index & is_positive_from_candidate_index ;
is_chase_from_candidate_index = is_test_positive_from_candidate_index & is_negative_from_candidate_index ;
is_ball_from_candidate_index = is_test_negative_from_candidate_index & is_negative_from_candidate_index ;
hit_count_within_candidates = sum(is_hit_from_candidate_index)
miss_count_within_candidates = sum(is_miss_from_candidate_index)
chase_count_within_candidates = sum(is_chase_from_candidate_index)
ball_count_within_candidates = sum(is_ball_from_candidate_index)
precision_within_candidates = hit_count_within_candidates / test_positive_count_within_candidates
recall_within_candidates = hit_count_within_candidates / positive_count_within_candidates
hit_rate_within_candidates = hit_count_within_candidates / positive_count_within_candidates
ball_rate_within_candidates = ball_count_within_candidates / negative_count_within_candidates
% break out the individual fields
voxel_count_from_guess_index = [feature_struct_from_guess_index.voxel_count]' ;
sqrt_condition_number_from_guess_index = [feature_struct_from_guess_index.sqrt_condition_number]' ;
max_intensity_from_guess_index = [feature_struct_from_guess_index.max_intensity]' ;
% Plot the targets in a projection of the feature space
f = figure('Color', 'w', 'Name', 'features') ;
a = axes(f) ;
h_ball = [] ;
% h_ball = plot3(max_intensity_from_candidate_index(is_ball_from_candidate_index), ...
% voxel_count_from_candidate_index(is_ball_from_candidate_index), ...
% sqrt_condition_number_from_candidate_index(is_ball_from_candidate_index), ...
% 'Marker', '.', 'Color', [0 0.8 0], 'LineStyle', 'none') ;
h_hit = plot3(max_intensity_from_candidate_index(is_hit_from_candidate_index), ...
voxel_count_from_candidate_index(is_hit_from_candidate_index), ...
sqrt_condition_number_from_candidate_index(is_hit_from_candidate_index), ...
'Marker', '.', 'Color', [0 0.3 1], 'LineStyle', 'none') ;
hold on ;
h_chase = plot3(max_intensity_from_candidate_index(is_chase_from_candidate_index), ...
voxel_count_from_candidate_index(is_chase_from_candidate_index), ...
sqrt_condition_number_from_candidate_index(is_chase_from_candidate_index), ...
'Marker', 'd', 'Color', [138 43 226]/255, 'LineStyle', 'none') ;
h_miss = plot3(max_intensity_from_candidate_index(is_miss_from_candidate_index), ...
voxel_count_from_candidate_index(is_miss_from_candidate_index), ...
sqrt_condition_number_from_candidate_index(is_miss_from_candidate_index), ...
'Marker', 'd', 'Color', [1 0 0], 'LineStyle', 'none') ;
hold off ;
xlabel('Max GFP signal (counts)') ;
ylabel('Voxel count') ;
zlabel('SD ratio') ;
%xlim([0 2^16]) ;
%ylim([0 2^16]) ;
handles = zeros(1,0) ;
legend_labels = cell(1,0) ;
if ~isempty(h_hit) ,
handles(1, end+1) = h_hit ;
legend_labels{1, end+1} = 'hit' ;
end
if ~isempty(h_miss) ,
handles(1, end+1) = h_miss ;
legend_labels{1, end+1} = 'miss' ;
end
if ~isempty(h_chase) ,
handles(1, end+1) = h_chase ;
legend_labels{1, end+1} = 'chase' ;
end
if ~isempty(h_ball) ,
handles(1, end+1) = h_ball ;
legend_labels{1, end+1} = 'ball' ;
end
legend(handles, legend_labels, 'Location', 'northwest') ;
%axis vis3d
grid on
%camproj perspective
% Plot the targets another projection of the feature space
f = figure('Color', 'w', 'Name', 'features') ;
a = axes(f) ;
h_hit = plot(max_intensity_from_candidate_index(is_hit_from_candidate_index), ...
max_background_intensity_from_candidate_index(is_hit_from_candidate_index), ...
'Marker', '.', 'Color', [0 0.3 1], 'LineStyle', 'none') ;
hold on ;
h_miss = plot(max_intensity_from_candidate_index(is_miss_from_candidate_index), ...
max_background_intensity_from_candidate_index(is_miss_from_candidate_index), ...
'Marker', 'd', 'Color', [1 0 0], 'LineStyle', 'none') ;
h_chase = plot(max_intensity_from_candidate_index(is_chase_from_candidate_index), ...
max_background_intensity_from_candidate_index(is_chase_from_candidate_index), ...
'Marker', 'd', 'Color', [138 43 226]/255, 'LineStyle', 'none') ;
h_ball = [] ;
% h_ball = plot(max_intensity_from_candidate_index(is_ball_from_candidate_index), ...
% max_background_intensity_from_candidate_index(is_ball_from_candidate_index), ...
% 'Marker', '.', 'Color', [0 0.8 0], 'LineStyle', 'none') ;
hold off ;
xlabel('Max GFP signal (counts)') ;
ylabel('Max background signal (counts)') ;
axis equal
xlim([0 2^16]) ;
ylim([0 2^16]) ;
handles = zeros(1,0) ;
legend_labels = cell(1,0) ;
if ~isempty(h_hit) ,
handles(1, end+1) = h_hit ;
legend_labels{1, end+1} = 'hit' ;
end
if ~isempty(h_miss) ,
handles(1, end+1) = h_miss ;
legend_labels{1, end+1} = 'miss' ;
end
if ~isempty(h_chase) ,
handles(1, end+1) = h_chase ;
legend_labels{1, end+1} = 'chase' ;
end
if ~isempty(h_ball) ,
handles(1, end+1) = h_ball ;
legend_labels{1, end+1} = 'ball' ;
end
legend(handles, legend_labels, 'Location', 'northwest') ;
grid on