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图像分割实战-系列教程7：unet医学细胞分割实战5（医学数据集、图像分割、语义分割、unet网络、代码逐行解读）

news 来源：原创 2024/9/22 13:32:39

🍁🍁🍁图像分割实战-系列教程总目录

有任何问题欢迎在下面留言
本篇文章的代码运行界面均在Pycharm中进行
本篇文章配套的代码资源已经上传

unet医学细胞分割实战1
unet医学细胞分割实战2
unet医学细胞分割实战3
unet医学细胞分割实战4
unet医学细胞分割实战5
unet医学细胞分割实战6

9 模型架构类----archs.py解读

这部分内容主要解析本任务使用的网络，主要有两个网络可以选择，一个是Unet另一个是NestedUNet，实际上就是UNet++，这两个网络的都是主要调用了VGG块来进行网络的构建

9.1 VGGBlock

import torch
from torch import nn
__all__ = ['UNet', 'NestedUNet']

class VGGBlock(nn.Module):def __init__(self, in_channels, middle_channels, out_channels):super().__init__()self.relu = nn.ReLU(inplace=True)self.conv1 = nn.Conv2d(in_channels, middle_channels, 3, padding=1)self.bn1 = nn.BatchNorm2d(middle_channels)self.conv2 = nn.Conv2d(middle_channels, out_channels, 3, padding=1)self.bn2 = nn.BatchNorm2d(out_channels)def forward(self, x):out = self.conv1(x)out = self.bn1(out)out = self.relu(out)out = self.conv2(out)out = self.bn2(out)out = self.relu(out)return out

首先来看看一个VGG块，实际上就是数据经过几个卷积relu：

输入数据
经过一个3*3的卷积
经过一个batchNormalization
经过一个relu
再次经过一个3*3的卷积
再次经过一个batchNormalization
再次经过一个relu
得到输出

这就是一个VGG块的过程，其中每次进入的数据的长宽、输出通道都是在调用VGG块的时候进行定义的，每一个VGG块有三个参数需要指定，分别是输入通道数、中间通道数、输出通道数

9.2 Unet

class UNet(nn.Module):def __init__(self, num_classes, input_channels=3, **kwargs):super().__init__()nb_filter = [32, 64, 128, 256, 512]self.pool = nn.MaxPool2d(2, 2)self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)self.conv0_0 = VGGBlock(input_channels, nb_filter[0], nb_filter[0])self.conv1_0 = VGGBlock(nb_filter[0], nb_filter[1], nb_filter[1])self.conv2_0 = VGGBlock(nb_filter[1], nb_filter[2], nb_filter[2])self.conv3_0 = VGGBlock(nb_filter[2], nb_filter[3], nb_filter[3])self.conv4_0 = VGGBlock(nb_filter[3], nb_filter[4], nb_filter[4])self.conv3_1 = VGGBlock(nb_filter[3]+nb_filter[4], nb_filter[3], nb_filter[3])self.conv2_2 = VGGBlock(nb_filter[2]+nb_filter[3], nb_filter[2], nb_filter[2])self.conv1_3 = VGGBlock(nb_filter[1]+nb_filter[2], nb_filter[1], nb_filter[1])self.conv0_4 = VGGBlock(nb_filter[0]+nb_filter[1], nb_filter[0], nb_filter[0])self.final = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1)def forward(self, input):x0_0 = self.conv0_0(input)x1_0 = self.conv1_0(self.pool(x0_0))x2_0 = self.conv2_0(self.pool(x1_0))x3_0 = self.conv3_0(self.pool(x2_0))x4_0 = self.conv4_0(self.pool(x3_0))x3_1 = self.conv3_1(torch.cat([x3_0, self.up(x4_0)], 1))x2_2 = self.conv2_2(torch.cat([x2_0, self.up(x3_1)], 1))x1_3 = self.conv1_3(torch.cat([x1_0, self.up(x2_2)], 1))x0_4 = self.conv0_4(torch.cat([x0_0, self.up(x1_3)], 1))output = self.final(x0_4)return output

Unet网络，主要都是调用VGG块来构建的：

首先输入数据
进入一个定义好的VGG块conv0_0 ，得到x0_0
x1_0、x2_0、x3_0、x4_0都是先经过一个(2,2)的maxpooling后，再经过一个定义好的VGG块
而x3_1、x2_2、x1_3、x0_4都是先与其对应的数据进行拼接后再经过一个定义好的VGG块，具体原理可以参考这篇文章
最后把x0_4的输出经过一个二维卷积得到最终的输出

9.3 NestedUNet

9.3.1 构造函数

class NestedUNet(nn.Module):def __init__(self, num_classes, input_channels=3, deep_supervision=False, **kwargs):super().__init__()nb_filter = [32, 64, 128, 256, 512]self.deep_supervision = deep_supervisionself.pool = nn.MaxPool2d(2, 2)self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)self.conv0_0 = VGGBlock(input_channels, nb_filter[0], nb_filter[0])self.conv1_0 = VGGBlock(nb_filter[0], nb_filter[1], nb_filter[1])self.conv2_0 = VGGBlock(nb_filter[1], nb_filter[2], nb_filter[2])self.conv3_0 = VGGBlock(nb_filter[2], nb_filter[3], nb_filter[3])self.conv4_0 = VGGBlock(nb_filter[3], nb_filter[4], nb_filter[4])self.conv0_1 = VGGBlock(nb_filter[0]+nb_filter[1], nb_filter[0], nb_filter[0])self.conv1_1 = VGGBlock(nb_filter[1]+nb_filter[2], nb_filter[1], nb_filter[1])self.conv2_1 = VGGBlock(nb_filter[2]+nb_filter[3], nb_filter[2], nb_filter[2])self.conv3_1 = VGGBlock(nb_filter[3]+nb_filter[4], nb_filter[3], nb_filter[3])self.conv0_2 = VGGBlock(nb_filter[0]*2+nb_filter[1], nb_filter[0], nb_filter[0])self.conv1_2 = VGGBlock(nb_filter[1]*2+nb_filter[2], nb_filter[1], nb_filter[1])self.conv2_2 = VGGBlock(nb_filter[2]*2+nb_filter[3], nb_filter[2], nb_filter[2])self.conv0_3 = VGGBlock(nb_filter[0]*3+nb_filter[1], nb_filter[0], nb_filter[0])self.conv1_3 = VGGBlock(nb_filter[1]*3+nb_filter[2], nb_filter[1], nb_filter[1])self.conv0_4 = VGGBlock(nb_filter[0]*4+nb_filter[1], nb_filter[0], nb_filter[0])if self.deep_supervision:self.final1 = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1)self.final2 = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1)self.final3 = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1)self.final4 = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1)else:self.final = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1)

9.3.2 前向传播

    def forward(self, input):x0_0 = self.conv0_0(input)x1_0 = self.conv1_0(self.pool(x0_0))x0_1 = self.conv0_1(torch.cat([x0_0, self.up(x1_0)], 1))x2_0 = self.conv2_0(self.pool(x1_0))x1_1 = self.conv1_1(torch.cat([x1_0, self.up(x2_0)], 1))x0_2 = self.conv0_2(torch.cat([x0_0, x0_1, self.up(x1_1)], 1))x3_0 = self.conv3_0(self.pool(x2_0))x2_1 = self.conv2_1(torch.cat([x2_0, self.up(x3_0)], 1))x1_2 = self.conv1_2(torch.cat([x1_0, x1_1, self.up(x2_1)], 1))x0_3 = self.conv0_3(torch.cat([x0_0, x0_1, x0_2, self.up(x1_2)], 1))x4_0 = self.conv4_0(self.pool(x3_0))x3_1 = self.conv3_1(torch.cat([x3_0, self.up(x4_0)], 1))x2_2 = self.conv2_2(torch.cat([x2_0, x2_1, self.up(x3_1)], 1))x1_3 = self.conv1_3(torch.cat([x1_0, x1_1, x1_2, self.up(x2_2)], 1))x0_4 = self.conv0_4(torch.cat([x0_0, x0_1, x0_2, x0_3, self.up(x1_3)], 1))if self.deep_supervision:output1 = self.final1(x0_1)output2 = self.final2(x0_2)output3 = self.final3(x0_3)output4 = self.final4(x0_4)return [output1, output2, output3, output4]else:output = self.final(x0_4)return output