3D残差U-Net是一种改进的深度学习模型，专为3D医学图像分割而设计。它结合了U-Net架构的强大特征提取能力与残差网络的优势，通过残差块增强梯度流动并缓解深层网络训练的梯度消失问题。3D卷积核可有效处理体积数据，保留空间信息，提高分割精度。下采样和上采样路径融合，提供多尺度特征，提升复杂结构的检测性能。其应用包括肿瘤检测、器官分割等，显著提高医学图像分析的自动化水平。
timeline
title Python和MATLAB及Julia示例3D残差U-Net
MATLAB: 残差模块
: 上采样层
: 跳跃连接
Python: 3D 卷积层
: 残差块
: U-Net 结构
: 跳跃连接
Julia: 3D 残差块
: U-Net 编码器和解码器模块
: 构建 3D 残差 U-Net
Python和C++胶体粒子三维残差算法模型和细化亚像素算法 | 亚图跨际
深度应用案例
在 Python 中实现 3D 残差 U-Net 涉及使用深度学习框架，如 PyTorch。3D 残差 U-Net 结合了 U-Net 的分割能力和残差网络的优势，适用于医学图像分割等需要处理三维数据的任务。下面是如何用 PyTorch 实现 3D 残差 U-Net 的详细代码。
我们将实现以下组件：
确保已安装 PyTorch，可以通过 pip install torch 安装。
import torch
import torch.nn as nn
import torch.nn.functional as F
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv3d(in_channels, out_channels, kernel_size, stride, padding)
self.bn1 = nn.BatchNorm3d(out_channels)
self.conv2 = nn.Conv3d(out_channels, out_channels, kernel_size, stride, padding)
self.bn2 = nn.BatchNorm3d(out_channels)
# 如果输入和输出通道数不同，需要调整捷径
if in_channels != out_channels:
self.shortcut = nn.Conv3d(in_channels, out_channels, kernel_size=1)
else:
self.shortcut = nn.Identity()
def forward(self, x):
shortcut = self.shortcut(x)
x = F.relu(self.bn1(self.conv1(x)))
x = self.bn2(self.conv2(x))
x += shortcut
return F.relu(x)
class UpConv(nn.Module):
def __init__(self, in_channels, out_channels):
super(UpConv, self).__init__()
self.up = nn.ConvTranspose3d(in_channels, out_channels, kernel_size=2, stride=2)
def forward(self, x):
return self.up(x)
class ResidualUNet3D(nn.Module):
def __init__(self, in_channels, out_channels):
super(ResidualUNet3D, self).__init__()
# 编码路径
self.enc1 = ResidualBlock(in_channels, 64)
self.pool1 = nn.MaxPool3d(2)
self.enc2 = ResidualBlock(64, 128)
self.pool2 = nn.MaxPool3d(2)
self.enc3 = ResidualBlock(128, 256)
self.pool3 = nn.MaxPool3d(2)
self.enc4 = ResidualBlock(256, 512)
# 瓶颈层
self.bottleneck = ResidualBlock(512, 1024)
# 解码路径
self.up3 = UpConv(1024, 512)
self.dec3 = ResidualBlock(1024, 512)
self.up2 = UpConv(512, 256)
self.dec2 = ResidualBlock(512, 256)
self.up1 = UpConv(256, 128)
self.dec1 = ResidualBlock(256, 128)
self.final_conv = nn.Conv3d(128, out_channels, kernel_size=1)
def forward(self, x):
# 编码路径
e1 = self.enc1(x)
p1 = self.pool1(e1)
e2 = self.enc2(p1)
p2 = self.pool2(e2)
e3 = self.enc3(p2)
p3 = self.pool3(e3)
e4 = self.enc4(p3)
# 瓶颈层
b = self.bottleneck(e4)
# 解码路径
d3 = self.up3(b)
d3 = torch.cat((d3, e4), dim=1)
d3 = self.dec3(d3)
d2 = self.up2(d3)
d2 = torch.cat((d2, e3), dim=1)
d2 = self.dec2(d2)
d1 = self.up1(d2)
d1 = torch.cat((d1, e2), dim=1)
d1 = self.dec1(d1)
out = self.final_conv(d1)
return out
# 示例实例化
model = ResidualUNet3D(in_channels=1, out_channels=2)
print(model)
UpConv 使用转置卷积进行上采样。