unet眼底血管分割matlab代码

UNet是一种常用于图像分割的深度学习网络结构，可以用于眼底血管分割。以下是一个基于Matlab实现的UNet眼底血管分割代码示例：

1. 首先需要准备眼底图像和标签图像，可以使用公开数据集或自己标注得到。

2. 加载数据并进行预处理

% 加载眼底图像和标签图像
img = imread('retina_img.png');
label = imread('retina_label.png');

% 将图像归一化到0-1之间
img = im2double(img);
label = im2double(label);

% 对图像和标签进行裁剪，使其大小是2的整数次幂
img = img(1:512, 1:512);
label = label(1:512, 1:512);

% 将标签图像转换为分类的one-hot编码
label = categorical(label, [0 1], [1 0]);

3. 定义UNet网络结构

% 定义UNet的编码器部分
encoder1 = convolution2dLayer(3, 64, 'Padding', 'same');
encoder1 = batchNormalizationLayer(encoder1);
encoder1 = reluLayer(encoder1);
encoder2 = maxPooling2dLayer(2, 'Stride', 2);
encoder2 = convolution2dLayer(3, 128, 'Padding', 'same');
encoder2 = batchNormalizationLayer(encoder2);
encoder2 = reluLayer(encoder2);
encoder3 = maxPooling2dLayer(2, 'Stride', 2);
encoder3 = convolution2dLayer(3, 256, 'Padding', 'same');
encoder3 = batchNormalizationLayer(encoder3);
encoder3 = reluLayer(encoder3);
encoder4 = maxPooling2dLayer(2, 'Stride', 2);
encoder4 = convolution2dLayer(3, 512, 'Padding', 'same');
encoder4 = batchNormalizationLayer(encoder4);
encoder4 = reluLayer(encoder4);

% 定义UNet的解码器部分
decoder1 = transposedConv2dLayer(2, 512, 'Stride', 2);
decoder1 = convolution2dLayer(3, 512, 'Padding', 'same');
decoder1 = batchNormalizationLayer(decoder1);
decoder1 = reluLayer(decoder1);
decoder2 = transposedConv2dLayer(2, 256, 'Stride', 2);
decoder2 = convolution2dLayer(3, 256, 'Padding', 'same');
decoder2 = batchNormalizationLayer(decoder2);
decoder2 = reluLayer(decoder2);
decoder3 = transposedConv2dLayer(2, 128, 'Stride', 2);
decoder3 = convolution2dLayer(3, 128, 'Padding', 'same');
decoder3 = batchNormalizationLayer(decoder3);
decoder3 = reluLayer(decoder3);
decoder4 = transposedConv2dLayer(2, 64, 'Stride', 2);
decoder4 = convolution2dLayer(3, 64, 'Padding', 'same');
decoder4 = batchNormalizationLayer(decoder4);
decoder4 = reluLayer(decoder4);

% 定义UNet的输出层
outputLayer = convolution2dLayer(1, 2, 'Padding', 'same');
outputLayer = softmaxLayer(outputLayer);
outputLayer = classificationLayer(outputLayer);

% 将编码器和解码器部分连接起来，形成UNet网络
layers = [
    encoder1
    encoder2
    encoder3
    encoder4
    decoder1
    decoder2
    decoder3
    decoder4
    outputLayer
];

4. 训练UNet网络

% 设置训练选项
options = trainingOptions('adam', ...
    'InitialLearnRate', 1e-3, ...
    'MaxEpochs', 30, ...
    'MiniBatchSize', 16, ...
    'Shuffle', 'every-epoch', ...
    'ValidationData', {img, label}, ...
    'ValidationFrequency', 5, ...
    'Plots', 'training-progress');

% 开始训练
net = trainNetwork(img, label, layers, options);

5. 使用UNet网络进行眼底血管分割

% 加载测试图像
testImg = imread('test_image.png');
testImg = im2double(testImg);

% 对测试图像进行预处理
testImg = testImg(1:512, 1:512);

% 使用UNet网络进行分割
predictedLabel = classify(net, testImg);

% 将预测结果转换为二值图像
predictedLabel = double(predictedLabel) - 1;
predictedLabel = imbinarize(predictedLabel);

% 显示原图像和分割结果
figure;
subplot(1, 2, 1);
imshow(testImg);
title('Original Image');
subplot(1, 2, 2);
imshow(predictedLabel);
title('Segmentation Result');

以上是一个基于Matlab实现的UNet眼底血管分割代码示例，可以根据实际需求进行调整和修改。