# Copyright 2018 University of Basel, Center for medical Image Analysis and Navigation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch as th
from numpy import inf,max
class _Registration():
def __init__(self, verbose=True):
# transformation of the image
self._transformation = None
# image similarity measure
self._image_loss = None
# optimizer
self._optimizer = None
self._number_of_iterations = 100
self._displacement = None
self._verbose=verbose
self.loss=inf
def set_optimizer(self, optimizer):
self._optimizer = optimizer
def set_number_of_iterations(self, number_of_iterations):
self._number_of_iterations = number_of_iterations
def set_transformation(self, transformation):
self._transformation = transformation
def set_image_loss(self, loss):
self._image_loss = loss
class _PairwiseRegistration(_Registration):
def __init__(self, verbose=True):
super(_PairwiseRegistration, self).__init__(verbose=verbose)
# regulariser on the displacement
self._regulariser_displacement = []
# regulariser on the parameters
self._regulariser_parameter = []
def set_regulariser_displacement(self, regulariser_displacement):
self._regulariser_displacement = regulariser_displacement
def set_regulariser_parameter(self, regulariser_parameter):
self._regulariser_parameter = regulariser_parameter
class _GroupwiseRegistration(_Registration):
def __init__(self, verbose=True):
super(_GroupwiseRegistration, self).__init__(verbose=verbose)
self._images = None
def SetImages(self, images):
self._images = images
class _ImageSeriesRegistration(_Registration):
def __init__(self, verbose=True):
super(_GroupwiseRegistration, self).__init__(verbose=verbose)
self._image_series = None
self._fixed_image = None
def SetImageSeries(self, images):
self._images = images
def SetFixedImage(self, image):
self._fixed_image = image
[docs]class PairwiseRegistration(_PairwiseRegistration):
def __init__(self, verbose=True):
super(PairwiseRegistration, self).__init__(verbose=verbose)
def _closure(self):
self._optimizer.zero_grad()
displacement = self._transformation()
# compute the image loss
lossList = []
loss_names = []
for image_loss in self._image_loss:
lossList.append(image_loss(displacement))
loss_names.append(image_loss.name)
# compute the regularisation loss on the displacement
for reg_disp in self._regulariser_displacement:
lossList.append(reg_disp(displacement))
loss_names.append(reg_disp.name)
# compute the regularisation loss on the parameter
for reg_param in self._regulariser_parameter:
lossList.append(reg_param(self._transformation.named_parameters()))
loss_names.append(reg_param.name)
if self._verbose:
for loss_value, loss_name in zip(lossList, loss_names):
print(str(loss_name) + ": " + str(loss_value.data.item()) + " ", end='', flush=True)
print("")
# sum up all loss terms
loss = sum(lossList)
loss.backward()
return loss
[docs] def start(self, EarlyStopping=False, StopPatience=10):
if EarlyStopping:
from copy import deepcopy
n = 0
try:
self.loss
except:
self.loss=inf
for iter_index in range(self._number_of_iterations):
if self._verbose:
print(str(iter_index) + " ", end='', flush=True)
loss = self._optimizer.step(self._closure)
if EarlyStopping:
if loss < self.loss:
n = 0
self.loss=loss
best=deepcopy(self._transformation)
else:
n += 1
if n > StopPatience:
self._transformation = best
return
self.loss = loss
[docs]class DemonsRegistraion(_Registration):
def __init__(self, verbose=True):
super(DemonsRegistraion, self).__init__(verbose=verbose)
# regulariser on the displacement
self._regulariser = []
[docs] def set_regulariser(self, regulariser):
self._regulariser = regulariser
def _closure(self):
self._optimizer.zero_grad()
displacement = self._transformation()
# compute the image loss
lossList = []
loss_names = []
for image_loss in self._image_loss:
lossList.append(image_loss(displacement))
loss_names.append(image_loss.name)
if self._verbose:
for loss_value, loss_name in zip(lossList, loss_names):
print(str(loss_name) + ": " + str(loss_value.data.item()) + " ", end='', flush=True)
print("")
# sum up all loss terms
loss = sum(lossList)
loss.backward()
return loss
[docs] def start(self):
for iter_index in range(self._number_of_iterations):
if self._verbose:
print(str(iter_index) + " ", end='', flush=True)
loss = self._optimizer.step(self._closure)
for regulariser in self._regulariser:
regulariser.regularise(self._transformation.parameters())