Res-uNet segmentation

### Res-U-Net分割模型的实现与应用 #### 背景介绍 Res-U-Net 是一种结合了残差网络 (ResNet) 和 U-Net 的架构，在医学图像处理和计算机视觉任务中表现出色。这种结构不仅继承了U-Net 结合了两个重要组件： 1. **编码器部分**：基于改进后的ResNet模块构建，能够有效捕捉输入图片的空间信息并逐步降低分辨率。 2. **解码器部分**：负责恢复空间维度的同时融合来自编码路径的不同层次特征图，从而提高最终预测的质量。 两者之间存在多层跳过链接（skip connections），允许低级细节直接传递到高级表示层面，这对于保持边界清晰至关重要[^2]。 #### 实现方法 下面展示了一个简单的PyTorch版本Res-U-Net框架示例代码: ```python import torch.nn as nn import torch class DoubleConv(nn.Module): """(convolution => [BN] => ReLU) * 2""" def __init__(self, in_channels, out_channels): super().__init__() self.double_conv = nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True), nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True) ) def forward(self, x): return self.double_conv(x) class DownBlock(nn.Module): """Downscaling with maxpool then double conv""" def __init__(self, in_channels, out_channels): super().__init__() self.maxpool_conv = nn.Sequential( nn.MaxPool2d(2), DoubleConv(in_channels, out_channels) ) def forward(self, x): return self.maxpool_conv(x) class UpBlock(nn.Module): """Upscaling then double conv""" def __init__(self, in_channels, out_channels, bilinear=True): super().__init__() if bilinear: self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) else: self.up = nn.ConvTranspose2d(in_channels // 2, in_channels // 2, kernel_size=2, stride=2) self.conv = DoubleConv(in_channels, out_channels) def forward(self, x1, x2): x1 = self.up(x1) diffY = x2.size()[2] - x1.size()[2] diffX = x2.size()[3] - x1.size()[3] x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2, diffY // 2, diffY - diffY // 2]) x = torch.cat([x2, x1], dim=1) return self.conv(x) class OutConv(nn.Module): def __init__(self, in_channels, out_channels): super(OutConv, self).__init__() self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1) def forward(self, x): return self.conv(x) class ResUNet(nn.Module): def __init__(n_classes): super().__init__() factor = 2 self.inc = DoubleConv(3, 64) self.down1 = DownBlock(64, 128) self.down2 = DownBlock(128, 256) self.down3 = DownBlock(256, 512) self.down4 = DownBlock(512, 1024 // factor) self.up1 = UpBlock(1024, 512 // factor, bilinear=False) self.up2 = UpBlock(512, 256 // factor, bilinear=False) self.up3 = UpBlock(256, 128 // factor, bilinear=False) self.up4 = UpBlock(128, 64, bilinear=False) self.outc = OutConv(64, n_classes) def forward(self, x): x1 = self.inc(x) x2 = self.down1(x1) x3 = self.down2(x2) x4 = self.down3(x3) x5 = self.down4(x4) x = self.up1(x5, x4) x = self.up2(x, x3) x = self.up3(x, x2) x = self.up4(x, x1) logits = self.outc(x) return logits ``` 此段代码定义了一个基本版的Res-U-Net神经网络类`ResUNet`, 它包含了卷积操作、池化以及上采样等必要组成部分，并利用跳跃连接来增强性能表现[^3].