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MSNet.py
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MSNet.py
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# -*- coding: utf-8 -*-
# Implementation of Wang et al 2017: Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks. https://arxiv.org/abs/1709.00382
# Author: Guotai Wang
# Copyright (c) 2017-2018 University College London, United Kingdom. All rights reserved.
# http://cmictig.cs.ucl.ac.uk
#
# Distributed under the BSD-3 licence. Please see the file licence.txt
# This software is not certified for clinical use.
#
from __future__ import absolute_import, print_function
import tensorflow as tf
from niftynet.layer.base_layer import TrainableLayer
from niftynet.layer import layer_util
from niftynet.layer.activation import ActiLayer
from niftynet.layer.bn import BNLayer
from niftynet.layer.convolution import ConvLayer, ConvolutionalLayer
from niftynet.layer.deconvolution import DeconvolutionalLayer
from niftynet.layer.elementwise import ElementwiseLayer
class MSNet(TrainableLayer):
"""
Inplementation of WNet, TNet and ENet presented in:
Wang, Guotai, Wenqi Li, Sebastien Ourselin, and Tom Vercauteren. "Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks." arXiv preprint arXiv:1709.00382 (2017).
These three variants are implemented in a single class named as "MSNet".
"""
def __init__(self,
num_classes,
w_initializer=None,
w_regularizer=None,
b_initializer=None,
b_regularizer=None,
acti_func='prelu',
name='MSNet'):
super(MSNet, self).__init__(name=name)
self.num_classes = num_classes
self.initializers = {'w': w_initializer, 'b': b_initializer}
self.regularizers = {'w': w_regularizer, 'b': b_regularizer}
self.acti_func = acti_func
self.base_chns = [32, 32, 32, 32]
self.downsample_twice = True
def set_params(self, params):
self.base_chns = params.get('base_feature_number', [32, 32, 32, 32])
self.acti_func = params.get('acti_func', 'prelu')
self.downsample_twice = params['downsample_twice']
def layer_op(self, images, is_training):
block1_1 = ResBlock(self.base_chns[0],
kernels = [[1, 3, 3], [1, 3, 3]],
acti_func=self.acti_func,
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
name = 'block1_1')
block1_2 = ResBlock(self.base_chns[0],
kernels = [[1, 3, 3], [1, 3, 3]],
acti_func=self.acti_func,
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
name = 'block1_2')
block2_1 = ResBlock(self.base_chns[1],
kernels = [[1, 3, 3], [1, 3, 3]],
acti_func=self.acti_func,
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
name = 'block2_1')
block2_2 = ResBlock(self.base_chns[1],
kernels = [[1, 3, 3], [1, 3, 3]],
acti_func=self.acti_func,
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
name = 'block2_2')
block3_1 = ResBlock(self.base_chns[2],
kernels = [[1, 3, 3], [1, 3, 3]],
dilation_rates = [[1, 1, 1], [1, 1, 1]],
acti_func=self.acti_func,
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
name = 'block3_1')
block3_2 = ResBlock(self.base_chns[2],
kernels = [[1, 3, 3], [1, 3, 3]],
dilation_rates = [[1, 2, 2], [1, 2, 2]],
acti_func=self.acti_func,
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
name = 'block3_2')
block3_3 = ResBlock(self.base_chns[2],
kernels = [[1, 3, 3], [1, 3, 3]],
dilation_rates = [[1, 3, 3], [1, 3, 3]],
acti_func=self.acti_func,
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
name = 'block3_3')
block4_1 = ResBlock(self.base_chns[3],
kernels = [[1, 3, 3], [1, 3, 3]],
dilation_rates = [[1, 3, 3], [1, 3, 3]],
acti_func=self.acti_func,
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
name = 'block4_1')
block4_2 = ResBlock(self.base_chns[3],
kernels = [[1, 3, 3], [1, 3, 3]],
dilation_rates = [[1, 2, 2], [1, 2, 2]],
acti_func=self.acti_func,
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
name = 'block4_2')
block4_3 = ResBlock(self.base_chns[3],
kernels = [[1, 3, 3], [1, 3, 3]],
dilation_rates = [[1, 1, 1], [1, 1, 1]],
acti_func=self.acti_func,
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
name = 'block4_3')
fuse1 = ConvolutionalLayer(self.base_chns[0],
kernel_size= [3, 1, 1],
padding='VALID',
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
b_initializer=self.initializers['b'],
b_regularizer=self.regularizers['b'],
acti_func=self.acti_func,
with_bn = True,
name='fuse1')
downsample1 = ConvolutionalLayer(self.base_chns[0],
kernel_size= [1, 3, 3],
stride = [1, 2, 2],
padding='SAME',
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
b_initializer=self.initializers['b'],
b_regularizer=self.regularizers['b'],
acti_func=self.acti_func,
with_bn = True,
name='downsample1')
fuse2 = ConvolutionalLayer(self.base_chns[1],
kernel_size= [3, 1, 1],
padding='VALID',
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
b_initializer=self.initializers['b'],
b_regularizer=self.regularizers['b'],
acti_func=self.acti_func,
with_bn = True,
name='fuse2')
downsample2 = ConvolutionalLayer(self.base_chns[1],
kernel_size= [1, 3, 3],
stride = [1, 2, 2],
padding='SAME',
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
b_initializer=self.initializers['b'],
b_regularizer=self.regularizers['b'],
acti_func=self.acti_func,
with_bn = True,
name='downsample2')
fuse3 = ConvolutionalLayer(self.base_chns[2],
kernel_size= [3, 1, 1],
padding='VALID',
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
b_initializer=self.initializers['b'],
b_regularizer=self.regularizers['b'],
acti_func=self.acti_func,
with_bn = True,
name='fuse3')
fuse4 = ConvolutionalLayer(self.base_chns[3],
kernel_size= [3, 1, 1],
padding='VALID',
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
b_initializer=self.initializers['b'],
b_regularizer=self.regularizers['b'],
acti_func=self.acti_func,
with_bn = True,
name='fuse4')
feature_expand1 = ConvolutionalLayer(self.base_chns[1],
kernel_size= [1, 1, 1],
stride = [1, 1, 1],
padding='SAME',
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
b_initializer=self.initializers['b'],
b_regularizer=self.regularizers['b'],
acti_func=self.acti_func,
with_bn = True,
name='feature_expand1')
feature_expand2 = ConvolutionalLayer(self.base_chns[2],
kernel_size= [1, 1, 1],
stride = [1, 1, 1],
padding='SAME',
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
b_initializer=self.initializers['b'],
b_regularizer=self.regularizers['b'],
acti_func=self.acti_func,
with_bn = True,
name='feature_expand2')
feature_expand3 = ConvolutionalLayer(self.base_chns[3],
kernel_size= [1, 1, 1],
stride = [1, 1, 1],
padding='SAME',
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
b_initializer=self.initializers['b'],
b_regularizer=self.regularizers['b'],
acti_func=self.acti_func,
with_bn = True,
name='feature_expand3')
centra_slice1 = TensorSliceLayer(margin = 2)
centra_slice2 = TensorSliceLayer(margin = 1)
pred1 = ConvLayer(self.num_classes,
kernel_size=[1, 3, 3],
padding = 'SAME',
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
b_initializer=self.initializers['b'],
b_regularizer=self.regularizers['b'],
name='pred1')
pred_up1 = DeconvolutionalLayer(self.num_classes,
kernel_size= [1, 3, 3],
stride = [1, 2, 2],
padding='SAME',
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
b_initializer=self.initializers['b'],
b_regularizer=self.regularizers['b'],
acti_func=self.acti_func,
with_bn = True,
name='pred_up1')
pred_up2_1 = DeconvolutionalLayer(self.num_classes*2,
kernel_size= [1, 3, 3],
stride = [1, 2, 2],
padding='SAME',
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
b_initializer=self.initializers['b'],
b_regularizer=self.regularizers['b'],
acti_func=self.acti_func,
with_bn = True,
name='pred_up2_1')
pred_up2_2 = DeconvolutionalLayer(self.num_classes*2,
kernel_size= [1, 3, 3],
stride = [1, 2, 2],
padding='SAME',
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
b_initializer=self.initializers['b'],
b_regularizer=self.regularizers['b'],
acti_func=self.acti_func,
with_bn = True,
name='pred_up2_2')
pred_up3_1 = DeconvolutionalLayer(self.num_classes*4,
kernel_size= [1, 3, 3],
stride = [1, 2, 2],
padding='SAME',
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
b_initializer=self.initializers['b'],
b_regularizer=self.regularizers['b'],
acti_func=self.acti_func,
with_bn = True,
name='pred_up3_1')
pred_up3_2 = DeconvolutionalLayer(self.num_classes*4,
kernel_size= [1, 3, 3],
stride = [1, 2, 2],
padding='SAME',
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
b_initializer=self.initializers['b'],
b_regularizer=self.regularizers['b'],
acti_func=self.acti_func,
with_bn = True,
name='pred_up3_2')
final_pred = ConvLayer(self.num_classes,
kernel_size=[1, 3, 3],
padding = 'SAME',
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
b_initializer=self.initializers['b'],
b_regularizer=self.regularizers['b'],
name='final_pred')
f1 = images
f1 = block1_1(f1, is_training)
f1 = block1_2(f1, is_training)
f1 = fuse1(f1, is_training)
if(self.downsample_twice):
f1 = downsample1(f1, is_training)
if(self.base_chns[0] != self.base_chns[1]):
f1 = feature_expand1(f1, is_training)
f1 = block2_1(f1, is_training)
f1 = block2_2(f1, is_training)
f1 = fuse2(f1, is_training)
f2 = downsample2(f1, is_training)
if(self.base_chns[1] != self.base_chns[2]):
f2 = feature_expand2(f2, is_training)
f2 = block3_1(f2, is_training)
f2 = block3_2(f2, is_training)
f2 = block3_3(f2, is_training)
f2 = fuse3(f2, is_training)
f3 = f2
if(self.base_chns[2] != self.base_chns[3]):
f3 = feature_expand3(f3, is_training)
f3 = block4_1(f3, is_training)
f3 = block4_2(f3, is_training)
f3 = block4_3(f3, is_training)
f3 = fuse4(f3, is_training)
p1 = centra_slice1(f1)
if(self.downsample_twice):
p1 = pred_up1(p1, is_training)
else:
p1 = pred1(p1)
p2 = centra_slice2(f2)
p2 = pred_up2_1(p2, is_training)
if(self.downsample_twice):
p2 = pred_up2_2(p2, is_training)
p3 = pred_up3_1(f3, is_training)
if(self.downsample_twice):
p3 = pred_up3_2(p3, is_training)
cat = tf.concat([p1, p2, p3], axis = 4, name = 'concate')
pred = final_pred(cat)
return pred
class ResBlock(TrainableLayer):
"""
This class define a high-resolution block with residual connections
kernels - specify kernel sizes of each convolutional layer
- e.g.: kernels=(5, 5, 5) indicate three conv layers of kernel_size 5
with_res - whether to add residual connections to bypass the conv layers
"""
def __init__(self,
n_output_chns,
kernels=[[1, 3, 3], [1, 3, 3]],
strides=[[1, 1, 1], [1, 1, 1]],
dilation_rates = [[1, 1, 1], [1, 1, 1]],
acti_func='prelu',
w_initializer=None,
w_regularizer=None,
with_res=True,
name='ResBlock'):
super(ResBlock, self).__init__(name=name)
self.n_output_chns = n_output_chns
if hasattr(kernels, "__iter__"): # a list of layer kernel_sizes
assert(len(kernels) == len(strides))
assert(len(kernels) == len(dilation_rates))
self.kernels = kernels
self.strides = strides
self.dilation_rates = dilation_rates
else: # is a single number (indicating single layer)
self.kernels = [kernels]
self.strides = [strides]
self.dilation_rates = [dilation_rates]
self.acti_func = acti_func
self.with_res = with_res
self.initializers = {'w': w_initializer}
self.regularizers = {'w': w_regularizer}
def layer_op(self, input_tensor, is_training):
output_tensor = input_tensor
for i in range(len(self.kernels)):
# create parameterised layers
bn_op = BNLayer(regularizer=self.regularizers['w'],
name='bn_{}'.format(i))
acti_op = ActiLayer(func=self.acti_func,
regularizer=self.regularizers['w'],
name='acti_{}'.format(i))
conv_op = ConvLayer(n_output_chns=self.n_output_chns,
kernel_size=self.kernels[i],
stride=self.strides[i],
dilation=self.dilation_rates[i],
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
name='conv_{}'.format(i))
# connect layers
output_tensor = bn_op(output_tensor, is_training)
output_tensor = acti_op(output_tensor)
output_tensor = conv_op(output_tensor)
# make residual connections
if self.with_res:
output_tensor = ElementwiseLayer('SUM')(output_tensor, input_tensor)
return output_tensor
class TensorSliceLayer(TrainableLayer):
"""
extract the central part of a tensor
"""
def __init__(self, margin = 1, regularizer=None, name='tensor_extract'):
self.layer_name = name
super(TensorSliceLayer, self).__init__(name=self.layer_name)
self.margin = margin
def layer_op(self, input_tensor):
input_shape = input_tensor.get_shape().as_list()
begin = [0]*len(input_shape)
begin[1] = self.margin
size = input_shape
size[1] = size[1] - 2* self.margin
output_tensor = tf.slice(input_tensor, begin, size, name='slice')
return output_tensor
if __name__ == '__main__':
x = tf.placeholder(tf.float32, shape = [1, 96, 96, 96, 1])
y = tf.placeholder(tf.float32, shape = [1, 96, 96, 96, 2])
net = MSNet(num_classes=2)
predicty = net(x, is_training = True)
print(x)
print(predicty)