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Copy pathCOPDGene_make_transform.py
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COPDGene_make_transform.py
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import argparse
import os
import random
import unittest
import footsteps
import icon_registration as icon
import icon_registration.data
import icon_registration.itk_wrapper
import icon_registration.networks as networks
import icon_registration.pretrained_models
import icon_registration.pretrained_models.lung_ct
import icon_registration.test_utils
import itk
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn.functional as F
import torchvision.utils
from icon_registration.config import device
footsteps.initialize(output_root="evaluation_results/")
import utils
input_shape = [1, 1, 175, 175, 175]
image_root = "/playpen-raid1/lin.tian/data/lung/dirlab_highres_350"
landmark_root = "/playpen-raid1/lin.tian/data/lung/reg_lung_2d_3d_1000_dataset_4_proj_clean_bg/landmarks/"
cases = [f"copd{i}_highres" for i in range(1, 11)]
parser = argparse.ArgumentParser()
parser.add_argument("weights_path")
parser.add_argument("--finetune", action="store_true")
args = parser.parse_args()
weights_path = args.weights_path
import equivariant_reg
#net = equivariant_reg.make_network_final(input_shape, 3)
threestep_consistent_net = equivariant_reg.make_network_final(input_shape, dimension=3, diffusion=False)
threestep_consistent_net.regis_net = icon.TwoStepRegistration(threestep_consistent_net.regis_net,
icon.FunctionFromVectorField(icon.networks.tallUNet2(dimension=3)))
threestep_consistent_net.assign_identity_map(input_shape)
net = threestep_consistent_net
import icon_registration.network_wrappers
utils.log(net.regis_net.load_state_dict(torch.load(weights_path), strict=False))
#net.regis_net = net.regis_net.phi
net.eval()
overall_1 = []
overall_2 = []
flips = []
ICON_errors=[]
for case in cases[:]:
image_insp = itk.imread(f"{image_root}/{case}/{case}_INSP_STD_COPD_img.nii.gz")
image_exp = itk.imread(f"{image_root}/{case}/{case}_EXP_STD_COPD_img.nii.gz")
seg_insp = itk.imread(f"{image_root}/{case}/{case}_INSP_STD_COPD_label.nii.gz")
seg_exp = itk.imread(f"{image_root}/{case}/{case}_EXP_STD_COPD_label.nii.gz")
landmarks_insp = icon_registration.test_utils.read_copd_pointset(
landmark_root + f"/{case.split('_')[0]}_300_iBH_xyz_r1.txt"
)
landmarks_exp = icon_registration.test_utils.read_copd_pointset(
landmark_root + f"/{case.split('_')[0]}_300_eBH_xyz_r1.txt"
)
image_insp_preprocessed = (
icon_registration.pretrained_models.lung_network_preprocess(
image_insp, seg_insp
)
)
image_exp_preprocessed = (
icon_registration.pretrained_models.lung_network_preprocess(image_exp, seg_exp)
)
# phi_AB, phi_BA, loss = icon_registration.itk_wrapper.register_pair(
# net,
# image_insp_preprocessed,
# image_exp_preprocessed,
# finetune_steps=(50 if args.finetune == True else None),
# return_artifacts=True,
# )
phi_BA, phi_AB, loss = icon_registration.itk_wrapper.register_pair(
net,
image_exp_preprocessed,
image_insp_preprocessed,
finetune_steps=(50 if args.finetune == True else None),
return_artifacts=True,
)
dists = []
for i in range(len(landmarks_exp)):
px, py = (
landmarks_insp[i],
np.array(phi_AB.TransformPoint(tuple(landmarks_exp[i]))),
)
dists.append(np.sqrt(np.sum((px - py) ** 2)))
utils.log(f"Mean error on {case}: ", np.mean(dists))
overall_1.append(np.mean(dists))
dists = []
for i in range(len(landmarks_insp)):
px, py = (
landmarks_exp[i],
np.array(phi_BA.TransformPoint(tuple(landmarks_insp[i]))),
)
dists.append(np.sqrt(np.sum((px - py) ** 2)))
utils.log(f"Mean error on {case}: ", np.mean(dists))
overall_2.append(np.mean(dists))
utils.log("flips:", loss.flips)
flips.append(loss.flips)
scale = 175
zz = (net.phi_AB(net.phi_BA(net.identity_map)) - net.identity_map) * scale
icon_error = torch.mean(torch.sqrt(torch.sum(zz**2, axis=1))).item()
ICON_errors.append(icon_error)
utils.log("ICON_error", icon_error)
itk.imwrite(image_insp, footsteps.output_dir + "moving_image.nrrd")
itk.imwrite(image_insp_preprocessed, footsteps.output_dir + "preprocessed_moving.nrrd")
itk.imwrite(image_exp, footsteps.output_dir + "fixed.nrrd")
itk.imwrite(image_exp_preprocessed, footsteps.output_dir + "preprocessed_fixed.nrrd")
itk.transformwrite([phi_AB], footsteps.output_dir + "transform.hdf5")
utils.log("mean ICON error", np.mean(ICON_errors))
utils.log("overall:")
utils.log(np.mean(overall_1))
utils.log(np.mean(overall_2))
utils.log("flips:", np.mean(flips))
utils.log("flips / prod(imnput_shape", np.mean(flips) / np.prod(input_shape))