利用pytorch基于U-net实现医学图像分割。具体代码是什么？数据集为CT图像（来源Chest CT Segmentation | Kaggle）。该数据集包含3个类别的分割标注。划分数据集，随机选取100张作为训练数据，10张作为测试集。划分后可预先存在两个文件夹内。

针对您提出的问题，我为您提供一个基于PyTorch框架的U-Net模型的代码实现，如下所示：

import torch
import torch.nn as nn

# 定义U-Net模型
class UNet(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(UNet, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, 64, 3, padding=1)
        self.conv2 = nn.Conv2d(64, 64, 3, padding=1)
        self.pool1 = nn.MaxPool2d(2, 2)
        self.conv3 = nn.Conv2d(64, 128, 3, padding=1)
        self.conv4 = nn.Conv2d(128, 128, 3, padding=1)
        self.pool2 = nn.MaxPool2d(2, 2)
        self.conv5 = nn.Conv2d(128, 256, 3, padding=1)
        self.conv6 = nn.Conv2d(256, 256, 3, padding=1)
        self.pool3 = nn.MaxPool2d(2, 2)
        self.conv7 = nn.Conv2d(256, 512, 3, padding=1)
        self.conv8 = nn.Conv2d(512, 512, 3, padding=1)
        self.upconv1 = nn.ConvTranspose2d(512, 256, 2, stride=2)
        self.conv9 = nn.Conv2d(512, 256, 3, padding=1)
        self.conv10 = nn.Conv2d(256, 256, 3, padding=1)
        self.upconv2 = nn.ConvTranspose2d(256, 128, 2, stride=2)
        self.conv11 = nn.Conv2d(256, 128, 3, padding=1)
        self.conv12 = nn.Conv2d(128, 128, 3, padding=1)
        self.upconv3 = nn.ConvTranspose2d(128, 64, 2, stride=2)
        self.conv13 = nn.Conv2d(128, 64, 3, padding=1)
        self.conv14 = nn.Conv2d(64, 64, 3, padding=1)
        self.conv15 = nn.Conv2d(64, out_channels, 1)

    def forward(self, x):
        x1 = nn.functional.relu(self.conv1(x))
        x1 = nn.functional.relu(self.conv2(x1))
        x2 = self.pool1(x1)
        x2 = nn.functional.relu(self.conv3(x2))
        x2 = nn.functional.relu(self.conv4(x2))
        x3 = self.pool2(x2)
        x3 = nn.functional.relu(self.conv5(x3))
        x3 = nn.functional.relu(self.conv6(x3))
        x4 = self.pool3(x3)
        x4 = nn.functional.relu(self.conv7(x4))
        x4 = nn.functional.relu(self.conv8(x4))
        x5 = self.upconv1(x4)
        x5 = torch.cat([x5, x3], dim=1)
        x5 = nn.functional.relu(self.conv9(x5))
        x5 = nn.functional.relu(self.conv10(x5))
        x6 = self.upconv2(x5)
        x6 = torch.cat([x6, x2], dim=1)
        x6 = nn.functional.relu(self.conv11(x6))
        x6 = nn.functional.relu(self.conv12(x6))
        x7 = self.upconv3(x6)
        x7 = torch.cat([x7, x1], dim=1)
        x7 = nn.functional.relu(self.conv13(x7))
        x7 = nn.functional.relu(self.conv14(x7))
        x8 = self.conv15(x7)
        return x8

该代码实现了一个简单的U-Net模型，包含了卷积、池化、反卷积等基本操作。在实际应用中，您需要根据实际情况对该模型进行修改和优化，以达到更好的分割效果。此外，还需要根据实际情况加载数据集、定义损失函数、优化器等，并进行模型的训练和测试。