帮我写一段基于模型展开的医学图像融合代码
时间: 2023-03-21 15:01:58 浏览: 79
医学图像融合是指将来自不同成像模态的医学图像进行融合,以增强诊断准确性。本文将介绍一种基于深度学习模型展开的医学图像融合方法,其中使用了 U-Net 网络模型。
首先,我们需要导入必要的 Python 库,包括 TensorFlow、NumPy 和 Matplotlib:
```python
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
```
接下来,我们定义 U-Net 模型的函数:
```python
def unet(input_shape):
inputs = tf.keras.layers.Input(input_shape)
# 编码器
conv1 = tf.keras.layers.Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
conv1 = tf.keras.layers.Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
pool1 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = tf.keras.layers.Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
conv2 = tf.keras.layers.Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
pool2 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = tf.keras.layers.Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
conv3 = tf.keras.layers.Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
pool3 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = tf.keras.layers.Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
conv4 = tf.keras.layers.Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
drop4 = tf.keras.layers.Dropout(0.5)(conv4)
pool4 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(drop4)
# 解码器
conv5 = tf.keras.layers.Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4)
conv5 = tf.keras.layers.Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
drop5 = tf.keras.layers.Dropout(0.5)(conv5)
up6 = tf.keras.layers.Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal')(tf.keras.layers.UpSampling2D(size=(2, 2))(drop5))
merge6 = tf.keras.layers.concatenate([drop4, up6], axis=3)
conv6 = tf.keras.layers.Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6)
conv6 = tf.keras.layers.Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv