U-Net医学图像分割实战

U-Net是一种用于医学图像分割的深度学习模型，它在2015年由Olaf Ronneberger等人提出。U-Net的结构类似于一个U形，因此得名，它基于卷积神经网络（CNN）的思想，使用反卷积层实现了图像的上采样，在这方面比其他图像分割模型更具优势。

下面是U-Net模型的结构：


U-Net模型分为两个部分：编码器和解码器。编码器部分由卷积层和最大池化层组成，在特征提取的同时缩小输入图像的大小。解码器部分由反卷积层和卷积层组成，将特征图像上采样到原始大小，并输出分割结果。

为了更好地理解U-Net模型，我们可以通过一个医学图像分割的实战来进一步学习。

## 实战：使用U-Net进行肝脏图像分割

### 数据集

我们使用了一个公共的医学图像分割数据集，名为MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge Data。该数据集包含131个肝脏CT图像，每个图像的大小为512x512，以及相应的肝脏和肝癌分割结果。

数据集可以从以下网址下载：https://competitions.codalab.org/competitions/17094

### 环境配置

- Python 3.6
- TensorFlow 1.14
- keras 2.2.4

### 数据预处理

在训练U-Net模型之前，我们需要对数据进行预处理。这里我们使用了一些常见的数据增强技术，包括旋转、翻转、缩放和随机裁剪等。

import numpy as np
import cv2
import os

def data_augmentation(image, label):
    if np.random.random() < 0.5:
        # rotate image and label
        angle = np.random.randint(-10, 10)
        rows, cols = image.shape[:2]
        M = cv2.getRotationMatrix2D((cols/2, rows/2), angle, 1)
        image = cv2.warpAffine(image, M, (cols, rows))
        label = cv2.warpAffine(label, M, (cols, rows))

    if np.random.random() < 0.5:
        # flip image and label
        image = cv2.flip(image, 1)
        label = cv2.flip(label, 1)

    if np.random.random() < 0.5:
        # scale image and label
        scale = np.random.uniform(0.8, 1.2)
        rows, cols = image.shape[:2]
        M = cv2.getRotationMatrix2D((cols/2, rows/2), 0, scale)
        image = cv2.warpAffine(image, M, (cols, rows), borderMode=cv2.BORDER_REFLECT)
        label = cv2.warpAffine(label, M, (cols, rows), borderMode=cv2.BORDER_REFLECT)

    if np.random.random() < 0.5:
        # crop image and label
        rows, cols = image.shape[:2]
        x = np.random.randint(0, rows - 256)
        y = np.random.randint(0, cols - 256)
        image = image[x:x+256, y:y+256]
        label = label[x:x+256, y:y+256]

    return image, label

def preprocess_data(image_path, label_path):
    image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE).astype(np.float32)
    label = cv2.imread(label_path, cv2.IMREAD_GRAYSCALE).astype(np.float32)

    # normalize image
    image = (image - np.mean(image)) / np.std(image)

    # resize image and label
    image = cv2.resize(image, (256, 256))
    label = cv2.resize(label, (256, 256))

    # perform data augmentation
    image, label = data_augmentation(image, label)

    # convert label to binary mask
    label[label > 0] = 1

    return image, label

### 构建U-Net模型

我们使用了Keras来构建U-Net模型，代码如下：

from keras.models import Model
from keras.layers import Input, Conv2D, MaxPooling2D, Dropout, UpSampling2D, concatenate

def unet(input_size=(256, 256, 1)):
    inputs = Input(input_size)

    # encoder
    conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
    conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

    conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
    conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)

    conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
    conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)

    conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
    conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
    drop4 = Dropout(0.5)(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)

    # decoder
    up5 = UpSampling2D(size=(2, 2))(pool4)
    up5 = Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal')(up5)
    merge5 = concatenate([drop4, up5], axis=3)
    conv5 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge5)
    conv5 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)

    up6 = UpSampling2D(size=(2, 2))(conv5)
    up6 = Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')(up6)
    merge6 = concatenate([conv3, up6], axis=3)
    conv6 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6)
    conv6 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)

    up7 = UpSampling2D(size=(2, 2))(conv6)
    up7 = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(up7)
    merge7 = concatenate([conv2, up7], axis=3)
    conv7 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7)
    conv7 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)

    up8 = UpSampling2D(size=(2, 2))(conv7)
    up8 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(up8)
    merge8 = concatenate([conv1, up8], axis=3)
    conv8 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8)
    conv8 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)

    outputs = Conv2D(1, 1, activation='sigmoid')(conv8)

    model = Model(inputs=inputs, outputs=outputs)
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

    return model

### 训练模型

我们将数据集分为训练集和测试集，然后使用Keras的fit方法来训练模型。

from keras.callbacks import ModelCheckpoint

# set paths
train_path = '/path/to/train'
test_path = '/path/to/test'

# get list of images and labels
train_images = sorted(os.listdir(os.path.join(train_path, 'images')))
train_labels = sorted(os.listdir(os.path.join(train_path, 'labels')))

test_images = sorted(os.listdir(os.path.join(test_path, 'images')))
test_labels = sorted(os.listdir(os.path.join(test_path, 'labels')))

# initialize model
model = unet()

# train model
checkpoint = ModelCheckpoint('model.h5', verbose=1, save_best_only=True)
model.fit_generator(generator(train_path, train_images, train_labels), steps_per_epoch=100, epochs=10, validation_data=generator(test_path, test_images, test_labels), validation_steps=50, callbacks=[checkpoint])

### 评估模型

训练完成后，我们需要对模型进行评估。这里我们使用了Dice系数和交并比（IoU）这两个常用的评估指标。

def dice_coef(y_true, y_pred):
    smooth = 1e-5
    y_true_f = K.flatten(y_true)
    y_pred_f = K.flatten(y_pred)
    intersection = K.sum(y_true_f * y_pred_f)
    return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)

def iou(y_true, y_pred):
    smooth = 1e-5
    y_true_f = K.flatten(y_true)
    y_pred_f = K.flatten(y_pred)
    intersection = K.sum(y_true_f * y_pred_f)
    union = K.sum(y_true_f) + K.sum(y_pred_f) - intersection
    return (intersection + smooth) / (union + smooth)

model = load_model('model.h5', custom_objects={'dice_coef': dice_coef, 'iou': iou})

test_images = sorted(os.listdir(os.path.join(test_path, 'images')))
test_labels = sorted(os.listdir(os.path.join(test_path, 'labels')))

dice_coefficients = []
ious = []

for i in range(len(test_images)):
    # preprocess image and label
    image_path = os.path.join(test_path, 'images', test_images[i])
    label_path = os.path.join(test_path, 'labels', test_labels[i])
    image, label = preprocess_data(image_path, label_path)

    # predict label
    pred = model.predict(np.expand_dims(image, axis=0))[0]

    # calculate dice coefficient and IoU
    dice_coefficient = dice_coef(np.expand_dims(label, axis=0), np.expand_dims(pred, axis=0))
    iou_ = iou(np.expand_dims(label, axis=0), np.expand_dims(pred, axis=0))

    dice_coefficients.append(dice_coefficient)
    ious.append(iou_)

# calculate average dice coefficient and IoU
print('Dice coefficient:', np.mean(dice_coefficients))
print('IoU:', np.mean(ious))

通过实战，我们可以更加深入地了解U-Net模型的原理和使用方法。