3d-unet

以下是一个简单的 3D Unet 模型的代码示例：

import tensorflow as tf
from tensorflow.keras.layers import Input, Conv3D, MaxPooling3D, Dropout, concatenate, UpSampling3D

def unet_3d(input_size=(128, 128, 128, 1)):
    inputs = Input(input_size)

    # 左边部分
    conv1 = Conv3D(32, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
    conv1 = Conv3D(32, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
    pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1)

    conv2 = Conv3D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
    conv2 = Conv3D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
    pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2)

    conv3 = Conv3D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
    conv3 = Conv3D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
    pool3 = MaxPooling3D(pool_size=(2, 2, 2))(conv3)

    conv4 = Conv3D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
    conv4 = Conv3D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
    drop4 = Dropout(0.5)(conv4)
    pool4 = MaxPooling3D(pool_size=(2, 2, 2))(drop4)

    # 右边部分
    conv5 = Conv3D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4)
    conv5 = Conv3D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
    drop5 = Dropout(0.5)(conv5)

    up6 = Conv3D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling3D(size=(2, 2, 2))(drop5))
    merge6 = concatenate([drop4, up6], axis=4)
    conv6 = Conv3D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6)
    conv6 = Conv3D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)

    up7 = Conv3D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling3D(size=(2, 2, 2))(conv6))
    merge7 = concatenate([conv3, up7], axis=4)
    conv7 = Conv3D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7)
    conv7 = Conv3D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)

    up8 = Conv3D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling3D(size=(2, 2, 2))(conv7))
    merge8 = concatenate([conv2, up8], axis=4)
    conv8 = Conv3D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8)
    conv8 = Conv3D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)

    up9 = Conv3D(32, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling3D(size=(2, 2, 2))(conv8))
    merge9 = concatenate([conv1, up9], axis=4)
    conv9 = Conv3D(32, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9)
    conv9 = Conv3D(32, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
    conv9 = Conv3D(2, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)

    # 输出
    outputs = Conv3D(1, 1, activation='sigmoid')(conv9)

    model = tf.keras.Model(inputs=inputs, outputs=outputs)

    return model

这是一个常见的 3D Unet 模型，与 2D Unet 模型类似，包括左边的下采样部分和右边的上采样部分。不同的是，在 3D Unet 中，我们使用了 3D 卷积层和 3D 池化层来逐渐减小特征图的大小，在上采样部分，我们使用了 3D 反卷积层和跳跃连接来逐渐增大特征图的大小，并将下采样部分相应的特征图与上采样部分的特征图进行拼接。最后，我们使用一个 3D 卷积层将 2 个通道的特征图转换为 1 个通道的输出。