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e4b9a7b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 | # Copyright 2020 MONAI Consortium
# 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.
"""
A reimplementation of CopleNet:
G. Wang, X. Liu, C. Li, Z. Xu, J. Ruan, H. Zhu, T. Meng, K. Li, N. Huang, S. Zhang. (2020)
"A Noise-robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions from CT Images."
IEEE Transactions on Medical Imaging. 2020. https://doi.org/10.1109/TMI.2020.3000314
Adapted from https://github.com/HiLab-git/COPLE-Net
"""
import torch
import torch.nn as nn
from monai.networks.blocks import Convolution, MaxAvgPool, ResidualSELayer, SimpleASPP, UpSample
from monai.networks.layers.factories import Act, Norm
from monai.utils import ensure_tuple_rep
class ConvBNActBlock(nn.Module):
"""Two convolution layers with batch norm, leaky relu, dropout and SE block"""
def __init__(self, in_channels, out_channels, dropout_p, spatial_dims: int = 2):
super().__init__()
self.conv_conv_se = nn.Sequential(
Convolution(spatial_dims, in_channels, out_channels, kernel_size=3, norm=Norm.BATCH, act=Act.LEAKYRELU),
nn.Dropout(dropout_p),
Convolution(spatial_dims, out_channels, out_channels, kernel_size=3, norm=Norm.BATCH, act=Act.LEAKYRELU),
ResidualSELayer(spatial_dims=spatial_dims, in_channels=out_channels, r=2),
)
def forward(self, x):
return self.conv_conv_se(x)
class DownBlock(nn.Module):
"""
Downsampling with a concatenation of max-pool and avg-pool, followed by ConvBNActBlock
"""
def __init__(self, in_channels, out_channels, dropout_p, spatial_dims: int = 2):
super().__init__()
self.max_avg_pool = MaxAvgPool(spatial_dims=spatial_dims, kernel_size=2)
self.conv = ConvBNActBlock(2 * in_channels, out_channels, dropout_p, spatial_dims=spatial_dims)
def forward(self, x):
x_pool = self.max_avg_pool(x)
return self.conv(x_pool) + x_pool
class UpBlock(nn.Module):
"""Upssampling followed by ConvBNActBlock"""
def __init__(self, in_channels1, in_channels2, out_channels, bilinear=True, dropout_p=0.5, spatial_dims: int = 2):
super().__init__()
self.up = UpSample(spatial_dims, in_channels1, in_channels2, scale_factor=2, with_conv=not bilinear)
self.conv = ConvBNActBlock(in_channels2 * 2, out_channels, dropout_p, spatial_dims=spatial_dims)
def forward(self, x1, x2):
x_cat = torch.cat([x2, self.up(x1)], dim=1)
return self.conv(x_cat) + x_cat
class CopleNet(nn.Module):
def __init__(
self,
spatial_dims: int = 2,
in_channels: int = 1,
out_channels: int = 2,
feature_channels=(32, 64, 128, 256, 512),
dropout=(0.0, 0.0, 0.3, 0.4, 0.5),
bilinear: bool = True,
):
"""
Args:
spatial_dims: dimension of the operators. Defaults to 2, i.e., using 2D operators
for all operators, for example, using Conv2D for all the convolutions.
It should be 2 for 3D images
in_channels: number of channels of the input image. Defaults to 1.
out_channels: number of segmentation classes (2 for foreground/background segmentation).
Defaults to 2.
feature_channels: number of intermediate feature channels
(must have 5 elements corresponding to five conv. stages).
Defaults to (32, 64, 128, 256, 512).
dropout: a sequence of 5 dropout ratios. Defaults to (0.0, 0.0, 0.3, 0.4, 0.5).
bilinear: whether to use bilinear upsampling. Defaults to True.
"""
super().__init__()
ft_chns = ensure_tuple_rep(feature_channels, 5)
f0_half = int(ft_chns[0] / 2)
f1_half = int(ft_chns[1] / 2)
f2_half = int(ft_chns[2] / 2)
f3_half = int(ft_chns[3] / 2)
self.in_conv = ConvBNActBlock(in_channels, ft_chns[0], dropout[0], spatial_dims)
self.down1 = DownBlock(ft_chns[0], ft_chns[1], dropout[1], spatial_dims)
self.down2 = DownBlock(ft_chns[1], ft_chns[2], dropout[2], spatial_dims)
self.down3 = DownBlock(ft_chns[2], ft_chns[3], dropout[3], spatial_dims)
self.down4 = DownBlock(ft_chns[3], ft_chns[4], dropout[4], spatial_dims)
self.bridge0 = Convolution(spatial_dims, ft_chns[0], f0_half, kernel_size=1, norm=Norm.BATCH, act=Act.LEAKYRELU)
self.bridge1 = Convolution(spatial_dims, ft_chns[1], f1_half, kernel_size=1, norm=Norm.BATCH, act=Act.LEAKYRELU)
self.bridge2 = Convolution(spatial_dims, ft_chns[2], f2_half, kernel_size=1, norm=Norm.BATCH, act=Act.LEAKYRELU)
self.bridge3 = Convolution(spatial_dims, ft_chns[3], f3_half, kernel_size=1, norm=Norm.BATCH, act=Act.LEAKYRELU)
self.up1 = UpBlock(ft_chns[4], f3_half, ft_chns[3], bilinear, dropout[3], spatial_dims)
self.up2 = UpBlock(ft_chns[3], f2_half, ft_chns[2], bilinear, dropout[2], spatial_dims)
self.up3 = UpBlock(ft_chns[2], f1_half, ft_chns[1], bilinear, dropout[1], spatial_dims)
self.up4 = UpBlock(ft_chns[1], f0_half, ft_chns[0], bilinear, dropout[0], spatial_dims)
self.aspp = SimpleASPP(
spatial_dims, ft_chns[4], int(ft_chns[4] / 4), kernel_sizes=[1, 3, 3, 3], dilations=[1, 2, 4, 6]
)
self.out_conv = Convolution(spatial_dims, ft_chns[0], out_channels, conv_only=True)
def forward(self, x):
x_shape = list(x.shape)
if len(x_shape) == 5:
[batch, chns, dim1, dim2, dim3] = x_shape
new_shape = [batch * dim1, chns, dim2, dim3]
x = torch.transpose(x, 1, 2)
x = torch.reshape(x, new_shape)
elif len(x_shape) == 3:
raise NotImplementedError("spatial dimension = 1 not supported.")
x0 = self.in_conv(x)
x0b = self.bridge0(x0)
x1 = self.down1(x0)
x1b = self.bridge1(x1)
x2 = self.down2(x1)
x2b = self.bridge2(x2)
x3 = self.down3(x2)
x3b = self.bridge3(x3)
x4 = self.down4(x3)
x4 = self.aspp(x4)
x = self.up1(x4, x3b)
x = self.up2(x, x2b)
x = self.up3(x, x1b)
x = self.up4(x, x0b)
output = self.out_conv(x)
if len(x_shape) == 5:
new_shape = [batch, dim1] + list(output.shape)[1:]
output = torch.reshape(output, new_shape)
output = torch.transpose(output, 1, 2)
return output
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