def __sample(self, iter):
target_img_batch, target_lab_batch, atlas_img_batch, atlas_lab_batch = self.validate_sampler.next_sample(
)
trainFeed = {
self.ph_target_image: target_img_batch,
self.ph_warp_label: atlas_lab_batch,
self.ph_gt_label: target_lab_batch
}
target_img, warp_label, gt, pred = self.sess.run([
self.ph_target_image, self.ph_warp_label, self.ph_gt_label,
self.predit
],
feed_dict=trainFeed)
sitk_write_images(target_img,
dir=self.args.sample_dir,
name=str(iter) + "target_img")
sitk_write_images(warp_label,
dir=self.args.sample_dir,
name=str(iter) + "warp_label")
sitk_write_labs(np.argmax(gt, axis=-1),
dir=self.args.sample_dir,
name=str(iter) + "gt")
sitk_write_labs(pred,
dir=self.args.sample_dir,
name=str(iter) + "pred")
acc = calculate_binary_dice(np.argmax(gt, axis=-1), pred)
self.logger.debug("acc:" + str(acc))
return acc
def sample(self, iter):
fix_imgs, fix_labs, mv_imgs, mv_labs = self.validate_sampler.next_sample(
)
trainFeed = {
self.ph_MV_image:
mv_imgs,
self.ph_FIX_image:
fix_imgs,
self.ph_MV_label:
mv_labs,
self.ph_FIX_label:
fix_labs,
self.ph_moving_affine:
util.initial_transform_generator(self.args.batch_size),
self.ph_fixed_affine:
util.initial_transform_generator(self.args.batch_size),
self.ph_random_ddf:
util.init_ddf_generator(self.args.batch_size, self.image_size)
}
input_mv_label, input_fix_label = self.sess.run(
[self.input_MV_label, self.input_FIX_label], feed_dict=trainFeed)
sitk_write_labs(input_fix_label, None, self.args.sample_dir,
'%d_input_fix_lab' % (iter))
sitk_write_labs(input_mv_label, None, self.args.sample_dir,
'%d_input_mv_lab' % (iter))
warp_mv_labs = self.sess.run(self.warped_MV_label, feed_dict=trainFeed)
# warp_mv_labs=np.where(warp_mv_labs>0.5,1,0)
sitk_write_labs(warp_mv_labs, None, self.args.sample_dir,
'%d_warp_mv_lab' % (iter))
warped_mv_imgs = self.sess.run(self.warped_MV_image,
feed_dict=trainFeed)
sitk_write_images(warped_mv_imgs, None, self.args.sample_dir,
'%d_warp_mv_img' % (iter))
sim_warp_mv_fix = calculate_binary_dice(warp_mv_labs, fix_labs)
ddf_mv_fix = self.sess.run(self.ddf_MV_FIX, feed_dict=trainFeed)
_, _, neg2 = neg_jac(ddf_mv_fix[0, ...])
self.logger.debug("global_step %d: dice : warp_mv_fix=%f" %
(iter, sim_warp_mv_fix))
self.logger.debug("neg_jac %d" % (neg2))
return sim_warp_mv_fix
def __sample(self, itr):
target_img_batch, target_lab_batch, atlas_img_batch, atlas_lab_batch, sim_batch = self.validate_sampler.next_sample(
)
trainFeed = {
self.ph_target_image: target_img_batch,
self.ph_target_label: target_lab_batch,
self.ph_atlas_image: atlas_img_batch,
self.ph_atlas_label: atlas_lab_batch,
self.ph_sim: sim_batch
}
target_img, atlas_img, gt_sim, pred = self.sess.run(
[
self.ph_target_image, self.ph_atlas_image, self.ph_sim,
self.prob
],
feed_dict=trainFeed)
sitk_write_labs(target_lab_batch,
dir=self.args.sample_dir,
name=str(itr) + "target_lab")
sitk_write_labs(atlas_lab_batch,
dir=self.args.sample_dir,
name=str(itr) + "atlas_lab")
sitk_write_images(target_img,
dir=self.args.sample_dir,
name=str(itr) + "target_img")
sitk_write_images(atlas_img,
dir=self.args.sample_dir,
name=str(itr) + "atlas_img")
sitk_write_images(gt_sim,
dir=self.args.sample_dir,
name=str(itr) + "gt_sim")
sitk_write_images(pred,
dir=self.args.sample_dir,
name=str(itr) + "pred_sim")
def __sample(self, itr):
target_img_batch, target_lab_batch, atlas_img_batch, atlas_lab_batch, sim_batch = self.validate_sampler.next_sample(
)
trainFeed = {
self.ph_target_image: target_img_batch,
self.ph_target_label: target_lab_batch,
self.ph_atlas_label: atlas_lab_batch,
self.ph_gt_dicesim: sim_batch
}
target_img, atlas_label, gt, target_lab, pred_lab, pred_sim = self.sess.run(
[
self.ph_target_image, self.ph_atlas_label, self.ph_gt_dicesim,
self.ph_target_label, self.predict_label, self.predict_sim
],
feed_dict=trainFeed)
sitk_write_images(target_img,
dir=self.args.sample_dir,
name=str(itr) + "target_img")
sitk_write_labs(atlas_label,
dir=self.args.sample_dir,
name=str(itr) + "atlas_label")
sitk_write_images(gt,
dir=self.args.sample_dir,
name=str(itr) + "gt_sim")
sitk_write_images(pred_sim,
dir=self.args.sample_dir,
name=str(itr) + "pred_sim")
sitk_write_labs(target_lab,
dir=self.args.sample_dir,
name=str(itr) + "target_label")
sitk_write_labs(pred_lab,
dir=self.args.sample_dir,
name=str(itr) + "pred_label")
pre_acc = calculate_binary_dice(target_lab, atlas_label)
acc = calculate_binary_dice(target_lab, pred_lab, 0.2)
self.logger.debug("pre_acc %f -> acc %f" % (pre_acc, acc))
return acc
def sample(self, iter):
fix_imgs, fix_labs, mv_imgs, mv_labs = self.validate_sampler.next_sample(
)
# p_img_mvs, p_img_fixs, p_lab_mvs, p_lab_fixs = self.validate_sampler.get_batch_file()
# fix_imgs, fix_labs, mv_imgs, mv_labs = self.validate_sampler.get_batch_data_V2(p_img_mvs, p_img_fixs, p_lab_mvs,
# p_lab_fixs)
trainFeed = self.create_feed_dict(fix_imgs,
fix_labs,
mv_imgs,
mv_labs,
is_aug=False)
input_mv_label, input_fix_label = self.sess.run(
[self.input_MV_label, self.input_FIX_label], feed_dict=trainFeed)
np.where(input_mv_label > 0.5, 1, 0)
np.where(input_fix_label > 0.5, 1, 0)
sitk_write_images(input_fix_label.astype(np.uint16), None,
self.args.sample_dir, '%d_input_fix_lab' % (iter))
sitk_write_images(input_mv_label.astype(np.uint16), None,
self.args.sample_dir, '%d_input_mv_lab' % (iter))
warp_mv_labs = self.sess.run(self.warped_MV_label, feed_dict=trainFeed)
warp_mv_labs = np.where(warp_mv_labs > 0.5, 1, 0)
sitk_write_images(warp_mv_labs.astype(np.uint16), None,
self.args.sample_dir, '%d_warp_mv_lab' % (iter))
fix_labs, mv_labs = self.sess.run(
[self.ph_FIX_label, self.ph_MV_label], feed_dict=trainFeed)
fix_labs = np.where(fix_labs > 0.5, 1, 0)
mv_labs = np.where(mv_labs > 0.5, 1, 0)
# sitk_write_images(fix_labs.astype(np.uint16), None, self.args.sample_dir, '%d_fix_lab' % (iter))
# sitk_write_images(mv_labs.astype(np.uint16),None,self.args.sample_dir,'%d_mv_lab'%(iter))
input_fix_imgs, input_mv_imgs = self.sess.run(
[self.input_FIX_image, self.input_MV_image], feed_dict=trainFeed)
sitk_write_images(input_fix_imgs, None, self.args.sample_dir,
'%d_input_fix_img' % (iter))
sitk_write_images(input_mv_imgs, None, self.args.sample_dir,
'%d_input_mv_img' % (iter))
warped_mv_imgs = self.sess.run(self.warped_MV_image,
feed_dict=trainFeed)
sitk_write_images(warped_mv_imgs, None, self.args.sample_dir,
'%d_warp_mv_img' % (iter))
fix_imgs, mv_imgs = self.sess.run(
[self.ph_FIX_image, self.ph_MV_image], feed_dict=trainFeed)
sitk_write_images(mv_imgs, None, self.args.sample_dir,
'%d_mv_img' % (iter))
sitk_write_images(fix_imgs, None, self.args.sample_dir,
'%d_fix_img' % (iter))
sim_warp_mv_fix = calculate_binary_dice(warp_mv_labs, fix_labs)
# para = sitk.ReadImage(p_lab_fixs[0])
# hd_warp_mv_fix = hd(np.squeeze(warp_mv_labs[0, ...]), np.squeeze(fix_labs[0, ...]),voxelspacing=para.GetSpacing())
hd_warp_mv_fix = 999
return sim_warp_mv_fix, hd_warp_mv_fix