PyTorch-UNet血管造影图像分割教程

### PyTorch UNet 血管造影 图像分割 教程 在医学图像处理领域，U-Net作为一种经典的卷积神经网络架构，已被广泛应用于多种任务，包括血管分割。以下是使用PyTorch实现基于U-Net的血管造影图像分割的一个完整教程。 #### 1. 数据准备 为了训练U-Net模型进行血管分割，需要准备好标注好的血管造影图像及其对应的掩码（mask）。这些数据应分为训练集和验证集[^1]。 ```python import torch from torchvision import transforms from torch.utils.data import DataLoader, Dataset class VesselDataset(Dataset): def __init__(self, image_paths, mask_paths, transform=None): self.image_paths = image_paths self.mask_paths = mask_paths self.transform = transform def __len__(self): return len(self.image_paths) def __getitem__(self, idx): image = Image.open(self.image_paths[idx]).convert('L') mask = Image.open(self.mask_paths[idx]).convert('L') if self.transform is not None: image = self.transform(image) mask = self.transform(mask) return image, mask # 定义数据预处理步骤 data_transforms = transforms.Compose([ transforms.Resize((256, 256)), transforms.ToTensor() ]) train_dataset = VesselDataset(train_image_paths, train_mask_paths, data_transforms) val_dataset = VesselDataset(val_image_paths, val_mask_paths, data_transforms) train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True) val_loader = DataLoader(val_dataset, batch_size=8, shuffle=False) ``` #### 2. U-Net 模型定义 U-Net由编码器（下采样路径）、解码器（上采样路径）以及跳跃连接组成。下面是一个简单的U-Net实现： ```python import torch.nn as nn import torch.nn.functional as F class DoubleConv(nn.Module): def __init__(self, in_channels, out_channels): super(DoubleConv, self).__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 UNet(nn.Module): def __init__(self, n_channels=1, n_classes=1): super(UNet, self).__init__() self.inc = DoubleConv(n_channels, 64) self.down1 = Down(64, 128) self.down2 = Down(128, 256) self.down3 = Down(256, 512) self.up1 = Up(512, 256) self.up2 = Up(256, 128) self.up3 = Up(128, 64) 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) x = self.up1(x4, x3) x = self.up2(x, x2) x = self.up3(x, x1) logits = self.outc(x) return logits class Down(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 Up(nn.Module): """Upscaling then double conv""" def __init__(self, in_channels, out_channels): super().__init__() self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) 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) ``` #### 3. 训练过程 设置损失函数、优化器并执行训练循环。 ```python device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = UNet().to(device) criterion = nn.BCEWithLogitsLoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) def train_model(model, criterion, optimizer, num_epochs=20): for epoch in range(num_epochs): model.train() running_loss = 0.0 for images, masks in train_loader: images, masks = images.to(device), masks.to(device).float() outputs = model(images) loss = criterion(outputs, masks.unsqueeze(1)) optimizer.zero_grad() loss.backward() optimizer.step() running_loss += loss.item() print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {running_loss/len(train_loader):.4f}") train_model(model, criterion, optimizer, num_epochs=20) ``` #### 4. 验证与评估 完成训练后，可以在验证集上测试模型性能。 ```python def evaluate_model(model, dataloader): model.eval() total_dice = 0.0 with torch.no_grad(): for images, masks in dataloader: images, masks = images.to(device), masks.to(device).float() preds = torch.sigmoid(model(images)) dice_score = (2 * (preds * masks).sum()) / ((preds + masks).sum() + 1e-8) total_dice += dice_score.item() avg_dice = total_dice / len(dataloader) print(f"Avg Dice Score on Validation Set: {avg_dice:.4f}") return avg_dice evaluate_model(model, val_loader) ``` --- ###