现有data文件下训练集图片volume文件,标签segmentation文件,验证集图片valvol文件,标签valseg文件,用MATLABR2019a写UNET分割代码
时间: 2023-06-27 07:07:06 浏览: 44
好的,我可以提供一个基于MATLAB R2019a的UNET分割代码,但需要先确认你是否已经安装了Deep Learning Toolbox和Image Processing Toolbox。
首先,我们需要加载训练数据和验证数据,可以使用MATLAB自带的ImageDatastore函数,代码如下:
```
% 加载训练数据
trainVol = imageDatastore('data/train/volume');
trainSeg = pixelLabelDatastore('data/train/segmentation', classes);
% 加载验证数据
valVol = imageDatastore('data/val/valvol');
valSeg = pixelLabelDatastore('data/val/valseg', classes);
```
其中,classes是一个包含所有标签的数组,比如:
```
classes = [
"background"
"liver"
];
```
接下来,我们需要定义UNET模型。UNET模型是一个基于卷积神经网络的图像分割模型,由编码器和解码器两部分组成。编码器通过卷积和池化操作将输入图像压缩为低维特征图,解码器则通过反卷积和上采样操作将低维特征图恢复为原始图像大小,并输出每个像素点的标签预测结果。代码如下:
```
% 定义UNET模型
numClasses = numel(classes);
inputSize = [256, 256, 1];
encoderDepth = 4;
encoderNumFilters = 64;
decoderNumFilters = 64;
dropoutProb = 0.5;
% 编码器部分
encoderLayers = [];
for i = 1:encoderDepth
convLayer = convolution2dLayer(3, encoderNumFilters, 'Padding', 'same');
reluLayer = reluLayer();
maxPoolLayer = maxPooling2dLayer(2, 'Stride', 2);
encoderLayers = [encoderLayers, convLayer, reluLayer, maxPoolLayer];
encoderNumFilters = encoderNumFilters * 2;
end
% 解码器部分
decoderLayers = [];
for i = 1:(encoderDepth-1)
upSampleLayer = transposedConv2dLayer(2, encoderNumFilters/2, 'Stride', 2);
concatLayer = concatenationLayer(3);
convLayer = convolution2dLayer(3, decoderNumFilters, 'Padding', 'same');
reluLayer = reluLayer();
dropoutLayer = dropoutLayer(dropoutProb);
decoderLayers = [decoderLayers, upSampleLayer, concatLayer, convLayer, reluLayer, dropoutLayer];
encoderNumFilters = encoderNumFilters / 2;
end
upSampleLayer = transposedConv2dLayer(2, encoderNumFilters/2, 'Stride', 2);
concatLayer = concatenationLayer(3);
convLayer = convolution2dLayer(3, numClasses, 'Padding', 'same');
softmaxLayer = softmaxLayer();
classificationLayer = pixelClassificationLayer('Classes', classes);
UNET = [
encoderLayers
decoderLayers
upSampleLayer
concatLayer
convLayer
softmaxLayer
classificationLayer
];
```
接下来,我们需要定义训练选项,包括学习率、迭代次数、批次大小等。代码如下:
```
% 训练选项
batchSize = 16;
initialLearningRate = 1e-3;
maxEpochs = 10;
l2Regularization = 1e-4;
validationFrequency = floor(numel(trainVol.Files)/batchSize);
options = trainingOptions('adam', ...
'InitialLearnRate', initialLearningRate, ...
'MaxEpochs', maxEpochs, ...
'MiniBatchSize', batchSize, ...
'Shuffle', 'every-epoch', ...
'ValidationData', {valVol, valSeg}, ...
'ValidationFrequency', validationFrequency, ...
'Verbose', true, ...
'Plots', 'training-progress', ...
'LearnRateSchedule', 'piecewise', ...
'LearnRateDropFactor', 0.1, ...
'LearnRateDropPeriod', 5, ...
'L2Regularization', l2Regularization);
```
最后,我们可以使用trainNetwork函数进行训练。代码如下:
```
% 训练UNET模型
[UNET, info] = trainNetwork(trainVol, trainSeg, UNET, options);
```
训练完成后,我们可以使用semanticseg函数进行测试和预测。代码如下:
```
% 测试UNET模型
testVol = imageDatastore('data/test/testvol');
testSeg = semanticseg(testVol, UNET);
% 显示预测结果
montage({testVol.read(), labeloverlay(testVol.read(), testSeg)})
```
以上就是一个基于MATLAB R2019a的UNET分割代码,希望能够对你有所帮助。