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bm.pred2.py
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bm.pred2.py
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import os
import nibabel as nib
import trimesh
import numpy as np
from tqdm import tqdm
from scipy.ndimage import distance_transform_cdt as cdt
from skimage.measure import marching_cubes
from skimage.measure import label as compute_cc
from skimage.filters import gaussian
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchdiffeq import odeint_adjoint as odeint
from data.preprocess import process_volume, process_surface, process_surface_inverse
from util.mesh import laplacian_smooth, compute_normal, compute_mesh_distance, check_self_intersect
from util.tca import topology
from model.net import CortexODE, Unet
from config import load_config
# initialize topology correction
topo_correct = topology()
def seg2surf(seg,
data_name='hcp',
sigma=0.5,
alpha=16,
level=0.8,
n_smooth=2):
"""
Extract the surface based on the segmentation.
seg: input segmentation
sigma: standard deviation of guassian blurring
alpha: threshold for obtaining boundary of topology correction
level: extracted surface level for Marching Cubes
n_smooth: iteration of Laplacian smoothing
"""
# ------ connected components checking ------
cc, nc = compute_cc(seg, connectivity=2, return_num=True)
cc_id = 1 + np.argmax(np.array([np.count_nonzero(cc == i)\
for i in range(1, nc+1)]))
seg = (cc==cc_id).astype(np.float64)
# ------ generate signed distance function ------
sdf = -cdt(seg) + cdt(1-seg)
sdf = sdf.astype(float)
sdf = gaussian(sdf, sigma=sigma)
# ------ topology correction ------
sdf_topo= topo_correct.apply(sdf, threshold=alpha)
# ------ marching cubes ------
v_mc, f_mc, _, _ = marching_cubes(-sdf_topo, level=-level, method='lorensen')
v_mc = v_mc[:,[2,1,0]].copy()
f_mc = f_mc.copy()
D1,D2,D3 = sdf_topo.shape
D = max(D1,D2,D3)
v_mc = (2*v_mc - [D3, D2, D1]) / D # rescale to [-1,1]
# ------ bias correction ------
# Note that this bias is introduced by FreeSurfer.
# FreeSurfer changed the size of the input MRI,
# but the affine matrix of the MRI was not changed.
# So this bias is caused by the different between
# the original and new affine matrix.
if data_name == 'hcp':
v_mc = v_mc + [0.0090, 0.0058, 0.0088]
elif data_name == 'adni':
v_mc = v_mc + [0.0090, 0.0000, 0.0095]
# ------ mesh smoothing ------
v_mc = torch.Tensor(v_mc).unsqueeze(0).to(device)
f_mc = torch.LongTensor(f_mc).unsqueeze(0).to(device)
for j in range(n_smooth): # smooth and inflate the mesh
v_mc = laplacian_smooth(v_mc, f_mc, 'uniform', lambd=1)
v_mc = v_mc[0].cpu().numpy()
f_mc = f_mc[0].cpu().numpy()
return v_mc, f_mc
if __name__ == '__main__':
# ------ load configuration ------
config = load_config()
test_type = config.test_type # initial surface / prediction / evaluation
data_dir = config.data_dir # directory of datasets
model_dir = config.model_dir # directory of pretrained models
init_dir = config.init_dir # directory for saving the initial surfaces
result_dir = config.result_dir # directory for saving the predicted surfaces
data_name = config.data_name # hcp, adni, dhcp
surf_hemi = config.surf_hemi # lh, rh
device = config.device
tag = config.tag # identity of the experiment
C = config.dim_h # hidden dimension of features
K = config.kernel_size # kernel / cube size
Q = config.n_scale # multi-scale input
step_size = config.step_size # step size of integration
solver = config.solver # ODE solver
n_inflate = config.n_inflate # inflation iterations
rho = config.rho # inflation scale
# ------ load models ------
segnet = Unet(c_in=1, c_out=3).to(device)
segnet.load_state_dict(torch.load(model_dir+'model_seg_'+data_name+'_'+tag+'.pt'))
if test_type == 'pred' or test_type == 'eval':
T = torch.Tensor([0,1]).to(device)
cortexode_wm = CortexODE(dim_in=3, dim_h=C, kernel_size=K, n_scale=Q).to(device)
cortexode_gm = CortexODE(dim_in=3, dim_h=C, kernel_size=K, n_scale=Q).to(device)
cortexode_wm.load_state_dict(torch.load(model_dir+'model_wm_'+data_name+'_'+surf_hemi+'_'+tag+'.pt', map_location=device))
cortexode_gm.load_state_dict(torch.load(model_dir+'model_gm_'+data_name+'_'+surf_hemi+'_'+tag+'.pt', map_location=device))
cortexode_wm.eval()
cortexode_gm.eval()
# ------ start testing ------
subject_list = sorted(os.listdir(data_dir))
if test_type == 'eval':
assd_wm_all = []
assd_gm_all = []
hd_wm_all = []
hd_gm_all = []
sif_wm_all = []
sif_gm_all = []
for i in tqdm(range(len(subject_list))):
subid = subject_list[i]
# ------- load brain MRI -------
if data_name == 'hcp' or data_name == 'adni':
brain = nib.load(data_dir+subid+'/mri/orig.mgz')
brain_arr = brain.get_fdata()
brain_arr = (brain_arr / 255.).astype(np.float32)
elif data_name == 'dhcp':
brain = nib.load(data_dir+subid+'/'+subid+'_T2w.nii.gz')
brain_arr = brain.get_fdata()
brain_arr = (brain_arr / 20).astype(np.float16)
brain_arr = process_volume(brain_arr, data_name)
volume_in = torch.Tensor(brain_arr).unsqueeze(0).to(device)
# ------- predict segmentation -------
with torch.no_grad():
seg_out = segnet(volume_in)
seg_pred = torch.argmax(seg_out, dim=1)[0]
if surf_hemi == 'lh':
seg = (seg_pred==1).cpu().numpy() # lh
elif surf_hemi == 'rh':
seg = (seg_pred==2).cpu().numpy() # rh
# ------- extract initial surface -------
v_in, f_in = seg2surf(seg, data_name, sigma=0.5,
alpha=16, level=0.8, n_smooth=2)
# ------- save initial surface -------
if test_type == 'init':
mesh_init = trimesh.Trimesh(v_in, f_in)
mesh_init.export(init_dir+'init_'+data_name+'_'+surf_hemi+'_'+subid+'.obj')
# ------- predict cortical surfaces -------
if test_type == 'pred' or test_type == 'eval':
with torch.no_grad():
v_in = torch.Tensor(v_in).unsqueeze(0).to(device)
f_in = torch.LongTensor(f_in).unsqueeze(0).to(device)
# wm surface
cortexode_wm.set_data(v_in, volume_in)
v_wm_pred = odeint(cortexode_wm, v_in, t=T, method=solver,
options=dict(step_size=step_size))[-1]
v_gm_in = v_wm_pred.clone()
# inflate and smooth
for i in range(2):
v_gm_in = laplacian_smooth(v_gm_in, f_in, lambd=1.0)
n_in = compute_normal(v_gm_in, f_in)
v_gm_in += 0.002 * n_in
# pial surface
cortexode_gm.set_data(v_gm_in, volume_in)
v_gm_pred = odeint(cortexode_gm, v_gm_in, t=T, method=solver,
options=dict(step_size=step_size/2))[-1] # divided by 2 to reduce SIFs
v_wm_pred = v_wm_pred[0].cpu().numpy()
f_wm_pred = f_in[0].cpu().numpy()
v_gm_pred = v_gm_pred[0].cpu().numpy()
f_gm_pred = f_in[0].cpu().numpy()
# map the surface coordinate from [-1,1] to its original space
v_wm_pred, f_wm_pred = process_surface_inverse(v_wm_pred, f_wm_pred, data_name)
v_gm_pred, f_gm_pred = process_surface_inverse(v_gm_pred, f_gm_pred, data_name)
# ------- save predictde surfaces -------
if test_type == 'pred':
### save mesh to .obj or .stl format by Trimesh
# mesh_wm = trimesh.Trimesh(v_wm_pred, f_wm_pred)
# mesh_gm = trimesh.Trimesh(v_gm_pred, f_gm_pred)
# mesh_wm.export(result_dir+'wm_'+data_name+'_'+surf_hemi+'_'+subid+'.stl')
# mesh_gm.export(result_dir+'gm_'+data_name+'_'+surf_hemi+'_'+subid+'.obj')
# save the surfaces in FreeSurfer format
nib.freesurfer.io.write_geometry(result_dir+data_name+'_'+surf_hemi+'_'+subid+'.white',
v_wm_pred, f_wm_pred)
nib.freesurfer.io.write_geometry(result_dir+data_name+'_'+surf_hemi+'_'+subid+'.pial',
v_gm_pred, f_gm_pred)
# ------- load ground truth surfaces -------
if test_type == 'eval':
if data_name == 'hcp':
v_wm_gt, f_wm_gt = nib.freesurfer.io.read_geometry(data_dir+subid+'/surf/'+surf_hemi+'.white.deformed')
v_gm_gt, f_gm_gt = nib.freesurfer.io.read_geometry(data_dir+subid+'/surf/'+surf_hemi+'.pial.deformed')
elif data_name == 'adni':
v_wm_gt, f_wm_gt = nib.freesurfer.io.read_geometry(data_dir+subid+'/surf/'+surf_hemi+'.white')
v_gm_gt, f_gm_gt = nib.freesurfer.io.read_geometry(data_dir+subid+'/surf/'+surf_hemi+'.pial')
elif data_name == 'dhcp':
if surf_hemi == 'lh':
surf_wm_gt = nib.load(data_dir+subid+'/'+subid+'_left_wm.surf.gii')
surf_gm_gt = nib.load(data_dir+subid+'/'+subid+'_left_pial.surf.gii')
v_wm_gt, f_wm_gt = surf_wm_gt.agg_data('pointset'), surf_wm_gt.agg_data('triangle')
v_gm_gt, f_gm_gt = surf_gm_gt.agg_data('pointset'), surf_gm_gt.agg_data('triangle')
elif surf_hemi == 'rh':
surf_wm_gt = nib.load(data_dir+subid+'/'+subid+'_right_wm.surf.gii')
surf_gm_gt = nib.load(data_dir+subid+'/'+subid+'_right_pial.surf.gii')
v_wm_gt, f_wm_gt = surf_wm_gt.agg_data('pointset'), surf_wm_gt.agg_data('triangle')
v_gm_gt, f_gm_gt = surf_gm_gt.agg_data('pointset'), surf_gm_gt.agg_data('triangle')
# apply the affine transformation provided by brain MRI nifti
v_tmp = np.ones([v_wm_gt.shape[0],4])
v_tmp[:,:3] = v_wm_gt
v_wm_gt = v_tmp.dot(np.linalg.inv(brain.affine).T)[:,:3]
v_tmp = np.ones([v_gm_gt.shape[0],4])
v_tmp[:,:3] = v_gm_gt
v_gm_gt = v_tmp.dot(np.linalg.inv(brain.affine).T)[:,:3]
# ------- evaluation -------
if test_type == 'eval':
v_wm_pred = torch.Tensor(v_wm_pred).unsqueeze(0).to(device)
f_wm_pred = torch.LongTensor(f_wm_pred).unsqueeze(0).to(device)
v_gm_pred = torch.Tensor(v_gm_pred).unsqueeze(0).to(device)
f_gm_pred = torch.LongTensor(f_gm_pred).unsqueeze(0).to(device)
v_wm_gt = torch.Tensor(v_wm_gt).unsqueeze(0).to(device)
f_wm_gt = torch.LongTensor(f_wm_gt.astype(np.float32)).unsqueeze(0).to(device)
v_gm_gt = torch.Tensor(v_gm_gt).unsqueeze(0).to(device)
f_gm_gt = torch.LongTensor(f_gm_gt.astype(np.float32)).unsqueeze(0).to(device)
# compute ASSD and HD
assd_wm, hd_wm = compute_mesh_distance(v_wm_pred, v_wm_gt, f_wm_pred, f_wm_gt)
assd_gm, hd_gm = compute_mesh_distance(v_gm_pred, v_gm_gt, f_gm_pred, f_gm_gt)
if data_name == 'dhcp': # the resolution is 0.7
assd_wm = 0.7*assd_wm
assd_gm = 0.7*assd_gm
hd_wm = 0.7*hd_wm
hd_gm = 0.7*hd_gm
assd_wm_all.append(assd_wm)
assd_gm_all.append(assd_gm)
hd_wm_all.append(hd_wm)
hd_gm_all.append(hd_gm)
### compute percentage of self-intersecting faces
### uncomment below if you have installed torch-mesh-isect
### https://github.com/vchoutas/torch-mesh-isect
# sif_wm_all.append(check_self_intersect(v_wm_pred, f_wm_pred, collisions=20))
# sif_gm_all.append(check_self_intersect(v_gm_pred, f_gm_pred, collisions=20))
sif_wm_all.append(0)
sif_gm_all.append(0)
# ------- report the final results -------
if test_type == 'eval':
print('======== wm ========')
print('assd mean:', np.mean(assd_wm_all))
print('assd std:', np.std(assd_wm_all))
print('hd mean:', np.mean(hd_wm_all))
print('hd std:', np.std(hd_wm_all))
print('sif mean:', np.mean(sif_wm_all))
print('sif std:', np.std(sif_wm_all))
print('======== gm ========')
print('assd mean:', np.mean(assd_gm_all))
print('assd std:', np.std(assd_gm_all))
print('hd mean:', np.mean(hd_gm_all))
print('hd std:', np.std(hd_gm_all))
print('sif mean:', np.mean(sif_gm_all))
print('sif std:', np.std(sif_gm_all))