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buildBrain_20181001.m
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buildBrain_20181001.m
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function [outputArgs] = buildBrain_20181001(configfile)
%BUILDBRAIN Aggregrates skeletonization results and build connectivity
%graph of the brain
%
% [OUTPUTARGS] = BUILDBRAIN(INPUTARGS) Explain usage here
%
% Inputs:
%
% Outputs:
%
% Examples:
%
% Provide sample usage code here
%
% See also: List related files here
% $Author: base $ $Date: 2016/05/17 17:19:01 $ $Revision: 0.1 $
% Copyright: HHMI 2016
if nargin<1
% configfile = './config_files/config_buildBrain_20180702_prob0_100.cfg';
configfile = './config_files/config_buildBrain_20181001_prob0.cfg';
% configfile = './config_files/config_buildBrain_20180309_prob1.cfg';
% configfile = './config_files/config_buildBrain_20170911_prob0.cfg';
% configfile = './config_files/config_buildBrain_20150619_octant12_prob0.cfg';
end
addpath(genpath('./common'))
addpath(genpath('./functions'))
opt = configparser(configfile);
if ~isfield(opt,'sampling')
opt.sampling = 'uni';
end
myh5 = opt.inputh5;
myh5prob = opt.h5prob;
[~,name] = fileparts(myh5);
h5infofile = fullfile('./h5infos',['h5inf_',name,'.mat']);
mkdir(fileparts(h5infofile))
[brainSize,RR,chunk_dims,rank] = h5parser(myh5,myh5prob);
%%
origin = h5read(opt.inputh5,[opt.h5prob,'_props/origin']);
spacing = h5read(opt.inputh5,[opt.h5prob,'_props/spacing']);
level = h5read(opt.inputh5,[opt.h5prob,'_props/level']);
params.outsiz = brainSize;
params.ox = origin(1);
params.oy = origin(2);
params.oz = origin(3);
params.sx = spacing(1);
params.sy = spacing(2);
params.sz = spacing(3);
params.level = level;
params.voxres = [params.sx params.sy params.sz]/2^(params.level)/1e3; % in um
opt.params = params;
%%
% create output folders
mkdir(fullfile(opt.outfolder,'full'))
mkdir(fullfile(opt.outfolder,'frags'))
%%
[subs,edges,A,weights] = skel2graph(opt);
subs_ori = subs;
edges_ori = edges;
weights_ori = weights;
A_ori = A;
%%
subs = subs_ori;
edges = edges_ori;
weights = weights_ori;
A = A_ori;
%% check if ROI exist
umROIs =[
[[74505.0, 14761.1, 31109.0] [75869.2, 15799.4, 32717.0]]
[[75766.8, 13685.6, 36329.9] [77657.1, 15957.7, 37616.3]]
];
if isempty(umROIs)
%%
Gin = graph(max(A,A'));
workflow1(Gin,subs,opt)
else
%%
opt_rois = opt;
opt_rois.outfolder = fullfile(opt_rois.outfolder,'ROIs');
% create output folders
mkdir(fullfile(opt_rois.outfolder,'full'))
mkdir(fullfile(opt_rois.outfolder,'frags'))
pixROIs = um2pix(params,reshape(umROIs',3,[])');
pixROIs = reshape(pixROIs',6,[])';
insubs = getIndsinROI(subs,pixROIs);
A_rois = A(insubs,:);A_rois = A_rois(:,insubs);
subs_rois = subs(insubs,:);
A_rois_complement = A(~insubs,:);
A_rois_complement = A_rois_complement(:,~insubs);
subs_rois_complement = subs(~insubs,:);
%%
opt_rois.writefull = 0;
opt_rois.writefrag = 1;
Gin_rois = graph(max(A_rois,A_rois'));
workflow1(Gin_rois,subs_rois,opt_rois)
%%
opt_rois.writefull = 1;
opt_rois.writefrag = 0;
Gin_rois_complement = graph(max(A_rois_complement,A_rois_complement'));
workflow1(Gin_rois_complement,subs_rois_complement,opt_rois)
%%
end
%%
maskids = []
% maskids = [56 672 1022 1031]; %ACB/Caudoputamen/Globus pallidus, external segment/Globus pallidus, internal segment
%maskids = [956 844 882 686 56 1022 1031 1021 1085 719 882 583 182305705 182305709 182305713]
gt_swcfolder = '/nrs/mouselight/seggui/swcfiles/GT/2017-09-25_striatum_neurons_temp'
if ~isempty(maskids)
%%
[hits_allen_brain] = maskWithAllenAtlas(params,subs_ori,maskids);
if exist('gt_swcfolder','var')
addpath(genpath('./scripts'))
[hits_gt,hits_delete,swcout,gtfile] = cropSectionBasedOnGT(params,gt_swcfolder,subs);
% keep allen_brain and gt then substract delete
keepthese = union(setdiff(hits_allen_brain,hits_delete),hits_gt);
figure, myplot3(subs(keepthese,:),'.')
else
keepthese = hits_allen_brain;
end
%%
subs = subs(keepthese,:);
A = A(keepthese,:);
A = A(:,keepthese);
end
%
% delete junks due to ventricle
[S,Comps] = graphconncomp(A,'DIRECTED',false);
Y = histcounts(Comps,1:S+1);
if 0
junk_locs = [[73336.7, 14360.0, 34718.8];
[71850.7, 14436.3, 34497.8];
[71870.7, 14308.7, 34508.5];
[73264.9, 14183.1, 34508.5];
[73328.9, 14801.0, 34508.5];
[71817.1, 14684.7, 34508.5];
[71875.3, 14339.0, 34448.2];
[73254.4, 14099.3, 34436.1];
[73366.1, 14453.1, 34753.0];
];
end
if exist('junk_locs','var')
figure(100)
cla
ic_ids_all = zeros(1,length(Comps));
for ilocs = 1:size(junk_locs,1)
[aa,bb] = min(sum(abs(subs-um2pix(params,junk_locs(ilocs,:))),2));
ic_ids=Comps==Comps(bb);
ic_ids_all = ic_ids_all|ic_ids;
hold on
subs_ = subs(ic_ids,:);
A_ = A(ic_ids,:); A_ = A_(:,ic_ids);
gplot3(A_,subs_,'-');
end
keepthese = find(~ic_ids_all);
subs = subs(keepthese,:);
A = A(keepthese,:);
A = A(:,keepthese);
end
%%
% junk_ids = junk_ids_070218sample();
junk_ids = [];
if exist('junk_ids','var')
%%
if opt.viz
figure(100)
cla
for ilocs = 1:size(junk_ids,1)
ic_ids=Comps==junk_ids(ilocs);
hold on
subs_ = subs(ic_ids,:);
A_ = A(ic_ids,:); A_ = A_(:,ic_ids);
gplot3(A_,subs_,'-');
end
end
ic_ids_all = zeros(1,length(Comps));
for ilocs = 1:size(junk_ids,1)
ic_ids=Comps==junk_ids(ilocs);
ic_ids_all = ic_ids_all|ic_ids;
end
%%
keepthese = find(~ic_ids_all);
subs = subs(keepthese,:);
A = A(keepthese,:);
A = A(:,keepthese);
end
%%
% [A,subs] = filterEdges(A,subs,params)
%
tstart = tic;
affinityBuilder(opt,A,subs)
sprintf('FINISHED IN: %d', round(toc(tstart)))
return
%%
Gin = graph(max(A,A'));
workflow1(Gin,subs,opt)
%% filter out results based on manual recons - this is for presentation only
if 0
%swcfolder = '/groups/mouseprojects/home/base/CODE/MATLAB/recontree/malazgirt/singlespace'
%%
% swcfolder = '/groups/mouseprojects/home/base/CODE/MATLAB/recontree/malazgirt/singlespace'
% swcfiles = dir([swcfolder,'/*.swc']);
swcfolder = '/groups/mousebrainmicro/mousebrainmicro/cluster/Reconstructions/2017-09-11/cropSWC'
swcfiles = dir(fullfile(swcfolder,'*.swc'))
numSWCs = length(swcfiles);
%%
DATA = [];
upDATA = [];
for ii=1:numSWCs
%%
[swcData,offset,color, header] = loadSWC(fullfile(swcfolder,swcfiles(ii).name));
swcData(:,3:5) = swcData(:,3:5) + ones(size(swcData,1),1)*offset;
swcData(:,3:5) = swcData(:,3:5)*1000;
%%
% upsample points that are consequtive
upthese = diff(swcData(:,7))==1;
pd=round(sqrt(sum(diff(swcData(:,3:5)).^2,2)));
idxup = find(upthese);
updata_ = [];
for jj=idxup(:)'
st = swcData(jj,:);
en = swcData(jj+1,:);
sl = en-st;
sp = [[1:1e3:pd(jj)-1 pd(jj)]/pd(jj)];
%updata = [updata sl(:)*sp+st(:)*ones(1,length(sp))];
updata_{jj} = [sl(:)*sp+st(:)*ones(1,length(sp))];
end
upDATA = cat(1,upDATA,[updata_{:}]');
DATA=cat(1,DATA,swcData);
end
%%
pixsDATA = um2pix(params,DATA(:,3:5)/1000);
pixsupdata = um2pix(params,upDATA(:,3:5)/1000);
pixs = pixsupdata;
% pixs = pixsDATA;
% pixs(pixs(:,2)<3000,:)=[];
%
[idxknn,dknn]=knnsearch(subs,pixs,'K',1);
validC = unique(Comps(idxknn(dknn<15)));
%%
if opt.viz % visualize clusters
%%
figure(22),
cla
plot3(pixs(:,1),pixs(:,2),pixs(:,3),'.k','MarkerSize',1)
hold on
axis equal tight
set(gca,'Ydir','reverse')
iter = 0;
for mC=ib(ia>100)%(1:100)%validC%1:size(Y,2)%ib(2:500)%
% for each cluster run reconstruction
if Y(mC)>opt.sizethreshold & ismember(mC,validC) %& ~(mC==ib(1))
% if Y(mC)>opt.sizethreshold & ismember(mC,validC)
iter = iter+1;
if ~rem(iter,round(length(validC)/100))
iter
end
subidx = find(Comps==mC);
plot3(subs(subidx,1),subs(subidx,2),subs(subidx,3),'.')
end
drawnow
end
end
else
validC = 1:size(Y,2);
end
%%
if 0
%%
figure(90),
plot3(subs(:,1),subs(:,2),subs(:,3),'.k','MarkerSize',.1)
hold on
axis equal tight
set(gca,'Ydir','reverse')
%%
figure(21),
cla
plot3(pixs(:,1),pixs(:,2),pixs(:,3),'.k','MarkerSize',.1)
hold on
axis equal tight
set(gca,'Ydir','reverse')
end
%%
if 0
if 0
reconComp(opt,validC,Y,A_,Comps,subs,params)
else
Gin = graph(max(A,A'));
workflow1(Gin,subs,opt)
end
else
tstart = tic;
affinityBuilder(opt,validC,Y,A_,Comps,subs)
sprintf('FINISHED IN: %d', round(toc(tstart)))
end
%%
% reconComptest(opt,validC,Y,A_,Comps,subs,params)
end