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test_LUNA.py
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test_LUNA.py
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import os
import numpy as np
import random as rn
import torch
import torch.nn as nn
from models.seq2seq_4DCT_voxelmorph import EncoderDecoderConvLSTM
import csv
from models.unet_model import Unet
#from models.seq2seq_ConvLSTM3d import EncoderDecoderConvLSTM
import matplotlib.pyplot as plt
import warnings
import SimpleITK as sitk
from models.Warp import Warp
#from scipy.ndimage import zoom
warnings.filterwarnings('ignore')
device = torch.device("cuda" if torch.cuda.is_available() else "cup")
#Loading image #
Data = np.load('./public_data/LUNA_imaging.npz')['Data']
#Loading Lung Mask#
Seq = np.load('./public_data/LUNA_mask.npz')['Data']
# Reading RPM #
with open('rpm_max.csv', 'r') as f:
data = list(csv.reader(f, delimiter=","))
RPM = np.array(data)
RPM = np.float32(RPM)
# Crop data #
test_x = Data[:,16:144,16:144,16:144,:]
test_sx = Seq[:,16:144,16:144,16:144,:]
test_RPM = RPM
Hidden_dim=96
ConvLSTMmodel = EncoderDecoderConvLSTM(nf=Hidden_dim, in_chan=1, size1=128, size2=128, size3=128)
ConvLSTMmodel.to(device)
ConvLSTMmodel.load_state_dict(torch.load('./trained_model/New_4DCT_epoch00141_train_loss0.0006_.model'))
Transform = Warp(size1=128, size2=128, size3=128)
Transform.to(device)
# Generate simulation for Each LUNA data #
for i in range(0,20):
patient = i
# Randomly choose RPM #
rpm = np.int(np.random.randint(0, 20, 1))
print("Patient index:", patient,"RPM index:",rpm )
test_x_ = test_x[patient,...]
test_x_ = np.expand_dims(test_x_, 0)
test_x_ = np.expand_dims(test_x_, 0) # 1,1,160,160,160
test_sx_ = test_sx[patient, ...]
test_sx_ = np.expand_dims(test_sx_, 0)
test_sx_ = torch.Tensor(test_sx_)
test_sx_ = test_sx_.permute(0,4,1,2,3)
test_sx_ = test_sx_.to(device)
test_rpm_ = test_RPM[rpm,:]
test_x_rpm = test_RPM[rpm,:1]
test_x_rpm = np.expand_dims(test_x_rpm,0)
test_y_rpm = test_RPM[rpm,0:]
test_y_rpm = np.expand_dims(test_y_rpm,0)
invol = torch.Tensor(test_x_)
invol = invol.permute(0, 1, 5, 2, 3, 4)
invol = invol.to(device)
test_x_rpm_tensor = torch.Tensor(test_x_rpm)
test_y_rpm_tensor = torch.Tensor(test_y_rpm)
test_x_rpm_tensor.to(device)
test_y_rpm_tensor.to(device)
# Prediction #
bat_pred, DVF = ConvLSTMmodel(invol, rpm_x=test_x_rpm_tensor, rpm_y=test_y_rpm_tensor, future_seq=9) # [1,2,3,176,176]
#bat_pred = bat_pred.cpu().detach().numpy()
#DVF = DVF.cpu().detach().numpy()
#bat_pred = np.squeeze(bat_pred)
# Contour propagation #
S2, S3, S4 = Transform(test_sx_, DVF[:,:,0,...]),Transform(test_sx_, DVF[:,:,1,...]),Transform(test_sx_, DVF[:,:,2,...])
S5, S6, S7 = Transform(test_sx_, DVF[:,:,3,...]),Transform(test_sx_, DVF[:,:,4,...]),Transform(test_sx_, DVF[:,:,5,...])
S8, S9, S10 = Transform(test_sx_, DVF[:,:,6,...]),Transform(test_sx_, DVF[:,:,7,...]),Transform(test_sx_, DVF[:,:,8,...])
S2, S3, S4 = S2.cpu().detach().numpy(), S3.cpu().detach().numpy(), S4.cpu().detach().numpy()
S5, S6, S7 = S5.cpu().detach().numpy(), S6.cpu().detach().numpy(), S7.cpu().detach().numpy()
S8, S9, S10 = S8.cpu().detach().numpy(), S9.cpu().detach().numpy(), S10.cpu().detach().numpy()
bat_pred = bat_pred.cpu().detach().numpy()
DVF = DVF.cpu().detach().numpy()
bat_pred = np.squeeze(bat_pred)
DVF = np.squeeze(DVF)
I1 = np.squeeze(test_x_[:,0, ...])
#ex = np.squeeze(test_x2_)
D2, D3, D4, D5 = DVF[:,0,...], DVF[:,1,...], DVF[:,2,...], DVF[:,3,...]
D6, D7, D8, D9, D10 = DVF[:,4,...], DVF[:,5,...], DVF[:,6,...], DVF[:,7,...],DVF[:,8,...]
pI2, pI3, pI4 = np.squeeze(bat_pred[0, ...]), np.squeeze(bat_pred[1, ...]), np.squeeze(bat_pred[2, ...])
pI5, pI6, pI7 = np.squeeze(bat_pred[3, ...]), np.squeeze(bat_pred[4, ...]), np.squeeze(bat_pred[5, ...])
pI8, pI9, pI10 = np.squeeze(bat_pred[6,...]), np.squeeze(bat_pred[7,...]), np.squeeze(bat_pred[8,...])
# Save results #
savepath = "./Results"
if not os.path.exists(savepath + "/" + "%3.3d" % patient):
os.makedirs(savepath + "/" + "%3.3d" % patient)
np.savetxt(savepath + "/" + "%3.3d" % patient + "/" + 'test_rpm.csv', test_rpm_, fmt="%1.4f", delimiter=",")
writer = sitk.ImageFileWriter()
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "inhale.nrrd")
writer.Execute(sitk.GetImageFromArray(I1))
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "inhale2_predict.nrrd")
writer.Execute(sitk.GetImageFromArray(pI2))
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "inhale3_predict.nrrd")
writer.Execute(sitk.GetImageFromArray(pI3))
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "inhale4_predict.nrrd")
writer.Execute(sitk.GetImageFromArray(pI4))
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "inhale5_predict.nrrd")
writer.Execute(sitk.GetImageFromArray(pI5))
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "inhale6_predict.nrrd")
writer.Execute(sitk.GetImageFromArray(pI6))
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "inhale7_predict.nrrd")
writer.Execute(sitk.GetImageFromArray(pI7))
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "inhale8_predict.nrrd")
writer.Execute(sitk.GetImageFromArray(pI8))
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "inhale9_predict.nrrd")
writer.Execute(sitk.GetImageFromArray(pI9))
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "inhale10_predict.nrrd")
writer.Execute(sitk.GetImageFromArray(pI10))
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "inhale_mask.nrrd")
writer.Execute(sitk.GetImageFromArray(np.squeeze(test_sx[patient, ...])))
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "inhale_mask2.nrrd")
writer.Execute(sitk.GetImageFromArray(S2))
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "inhale_mask3.nrrd")
writer.Execute(sitk.GetImageFromArray(S3))
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "inhale_mask4.nrrd")
writer.Execute(sitk.GetImageFromArray(S4))
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "inhale_mask5.nrrd")
writer.Execute(sitk.GetImageFromArray(S5))
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "inhale_mask6.nrrd")
writer.Execute(sitk.GetImageFromArray(S6))
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "inhale_mask7.nrrd")
writer.Execute(sitk.GetImageFromArray(S7))
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "inhale_mask8.nrrd")
writer.Execute(sitk.GetImageFromArray(S8))
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "inhale_mask9.nrrd")
writer.Execute(sitk.GetImageFromArray(S9))
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "inhale_mask10.nrrd")
writer.Execute(sitk.GetImageFromArray(S10))
# Permute DVF #
def dvf_(d):
x = d[0,...]
x = np.reshape(x, [1,128,128,128])
y = d[1,...]
y = np.reshape(y, [1,128,128,128])
z = d[2,...]
z = np.reshape(z, [1,128,128,128])
out = np.concatenate([z,y,x],0)
return out
DVF2, DVF3, DVF4, DVF5 = dvf_(DVF[:,0,...]), dvf_(DVF[:,1,...]), dvf_(DVF[:,2,...]), dvf_(DVF[:,3,...])
DVF6, DVF7, DVF8, DVF9, DVF10 = dvf_(DVF[:,4,...]), dvf_(DVF[:,5,...]), dvf_(DVF[:,6,...]), dvf_(DVF[:,7,...]),dvf_(DVF[:,8,...])
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "DVF2.nrrd")
writer.Execute(sitk.GetImageFromArray(np.transpose(dvf_(DVF2), [1,2,3,0]))) # 3 1 2
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "DVF3.nrrd")
writer.Execute(sitk.GetImageFromArray(np.transpose(dvf_(DVF3), [1,2,3,0]))) # 3 1 2
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "DVF4.nrrd")
writer.Execute(sitk.GetImageFromArray(np.transpose(dvf_(DVF4), [1,2,3,0]))) # 3 1 2
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "DVF5.nrrd")
writer.Execute(sitk.GetImageFromArray(np.transpose(dvf_(DVF5), [1,2,3,0]))) # 3 1 2
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "DVF6.nrrd")
writer.Execute(sitk.GetImageFromArray(np.transpose(dvf_(DVF6), [1,2,3,0]))) # 3 1 2
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "DVF7.nrrd")
writer.Execute(sitk.GetImageFromArray(np.transpose(dvf_(DVF7), [1,2,3,0]))) # 3 1 2
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "DVF8.nrrd")
writer.Execute(sitk.GetImageFromArray(np.transpose(dvf_(DVF8), [1,2,3,0]))) # 3 1 2
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "DVF9.nrrd")
writer.Execute(sitk.GetImageFromArray(np.transpose(dvf_(DVF9), [1,2,3,0]))) # 3 1 2
writer.SetFileName(savepath + "/" + "%3.3d" % patient + "/" + "DVF10.nrrd")
writer.Execute(sitk.GetImageFromArray(np.transpose(dvf_(DVF10), [1,2,3,0]))) # 3 1 2