### 脑肿瘤分割与3D可视化Python脚本 对于脑肿瘤分割任务，UNet架构因其强大的性能而被广泛采用。该模型能够处理复杂的医疗影像数据并提供精确的分割结果[^1]。 下面展示了一个简化版的基于PyTorch框架的3D UNet实现以及如何利用`plotly`库来进行三维可视化： #### 3D UNet 实现 ```python import torch from torch import nn class DoubleConv(nn.Module): """(convolution => [BN] => ReLU) * 2""" def __init__(self, in_channels, out_channels): super().__init__() self.double_conv = nn.Sequential( nn.Conv3d(in_channels, out_channels, kernel_size=3, padding=1), nn.BatchNorm3d(out_channels), nn.ReLU(inplace=True), nn.Conv3d(out_channels, out_channels, kernel_size=3, padding=1), nn.BatchNorm3d(out_channels), nn.ReLU(inplace=True) ) def forward(self, x): return self.double_conv(x) class UNet_3D(nn.Module): def __init__(self, n_channels, n_classes): super(UNet_3D, self).__init__() bilinear = True factor = 2 if bilinear else 1 self.inc = DoubleConv(n_channels, 64) self.down1 = Down(64, 128) self.down2 = Down(128, 256) self.down3 = Down(256, 512 // factor) self.up1 = Up(512, 256 // factor, bilinear) self.up2 = Up(256, 128 // factor, bilinear) self.up3 = Up(128, 64, bilinear) 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, x2) logits = self.outc(x) return logits ``` 此代码片段定义了基本的3D UNet结构，其中包含了双层卷积模块(Double Convolution)，下采样路径(down path), 和上采样路径(up path)[^2]。 #### 使用Plotly进行3D可视化 为了更好地理解预测结果，在完成分割之后可以使用如下方法创建交互式的3D视图: ```python import numpy as np import plotly.graph_objects as go def visualize_segmentation(pred_mask): """ Visualize the predicted mask using Plotly. :param pred_mask: Numpy array containing prediction results with shape (depth, height, width). """ z, y, x = np.where(pred_mask != 0) fig = go.Figure(data=[go.Scatter3d( x=x, y=y, z=z, mode='markers', marker=dict( size=2, color=pred_mask[z,y,x], # set color to an array/list of desired values colorscale='Viridis', # choose a colorscale opacity=0.8 ) )]) fig.update_layout(scene=dict( xaxis_title='Width', yaxis_title='Height', zaxis_title='Depth'), margin=dict(r=20, l=10, b=10, t=10)) fig.show() ``` 上述函数接受一个表示预测掩模(numpy数组形式)作为参数，并通过散点图的方式渲染出各个体素的位置及其对应的类别标签颜色。