1.数据集

肝脏分割数据集，训练集一共有400张肝脏CT图像以及对应的分割模板，验证集一共有20张肝脏CT图像以及对应的分割模板，如下图所示

2.代码

model.py

from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Activation, BatchNormalization, Conv2D, Conv2DTranspose, MaxPooling2D, concatenate#定义U-Net网络模型
def unet(input_size=(416, 416, 3)):inputs = Input(input_size)conv1 = Conv2D(64, (3, 3), padding='same')(inputs)bn1 = Activation('relu')(conv1)conv1 = Conv2D(64, (3, 3), padding='same')(bn1)bn1 = BatchNormalization(axis=3)(conv1)bn1 = Activation('relu')(bn1)pool1 = MaxPooling2D(pool_size=(2, 2))(bn1)conv2 = Conv2D(128, (3, 3), padding='same')(pool1)bn2 = Activation('relu')(conv2)conv2 = Conv2D(128, (3, 3), padding='same')(bn2)bn2 = BatchNormalization(axis=3)(conv2)bn2 = Activation('relu')(bn2)pool2 = MaxPooling2D(pool_size=(2, 2))(bn2)conv3 = Conv2D(256, (3, 3), padding='same')(pool2)bn3 = Activation('relu')(conv3)conv3 = Conv2D(256, (3, 3), padding='same')(bn3)bn3 = BatchNormalization(axis=3)(conv3)bn3 = Activation('relu')(bn3)pool3 = MaxPooling2D(pool_size=(2, 2))(bn3)conv4 = Conv2D(512, (3, 3), padding='same')(pool3)bn4 = Activation('relu')(conv4)conv4 = Conv2D(512, (3, 3), padding='same')(bn4)bn4 = BatchNormalization(axis=3)(conv4)bn4 = Activation('relu')(bn4)pool4 = MaxPooling2D(pool_size=(2, 2))(bn4)conv5 = Conv2D(1024, (3, 3), padding='same')(pool4)bn5 = Activation('relu')(conv5)conv5 = Conv2D(1024, (3, 3), padding='same')(bn5)bn5 = BatchNormalization(axis=3)(conv5)bn5 = Activation('relu')(bn5)up6 = concatenate([Conv2DTranspose(512, (2, 2), strides=(2, 2), padding='same')(bn5), conv4], axis=3)conv6 = Conv2D(512, (3, 3), padding='same')(up6)bn6 = Activation('relu')(conv6)conv6 = Conv2D(512, (3, 3), padding='same')(bn6)bn6 = BatchNormalization(axis=3)(conv6)bn6 = Activation('relu')(bn6)up7 = concatenate([Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(bn6), conv3], axis=3)conv7 = Conv2D(256, (3, 3), padding='same')(up7)bn7 = Activation('relu')(conv7)conv7 = Conv2D(256, (3, 3), padding='same')(bn7)bn7 = BatchNormalization(axis=3)(conv7)bn7 = Activation('relu')(bn7)up8 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(bn7), conv2], axis=3)conv8 = Conv2D(128, (3, 3), padding='same')(up8)bn8 = Activation('relu')(conv8)conv8 = Conv2D(128, (3, 3), padding='same')(bn8)bn8 = BatchNormalization(axis=3)(conv8)bn8 = Activation('relu')(bn8)up9 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(bn8), conv1], axis=3)conv9 = Conv2D(64, (3, 3), padding='same')(up9)bn9 = Activation('relu')(conv9)conv9 = Conv2D(64, (3, 3), padding='same')(bn9)bn9 = BatchNormalization(axis=3)(conv9)bn9 = Activation('relu')(bn9)conv10 = Conv2D(1, (1, 1), activation='sigmoid')(bn9)return Model(inputs=[inputs], outputs=[conv10])

train.py

import os
from model import unet
import tensorflow as tf
from tensorflow.keras.callbacks import ReduceLROnPlateau
from tensorflow.keras import backend as K
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.callbacks import ModelCheckpoint#图片大小
im_width = 416
im_height = 416BATCH_SIZE = 8train_files = './data/train/'
val_files = './data/val/'if not os.path.exists("save_weights"):os.makedirs("save_weights")train_num = len(os.listdir(os.path.join(train_files,'image')))
val_num = len(os.listdir(os.path.join(val_files,'image')))# 定义数据生成器函数
def data_generator(data_path, batch_size, aug_dict,image_color_mode="rgb",mask_color_mode="grayscale",image_save_prefix="image",mask_save_prefix="mask",save_to_dir=None,target_size=(im_width, im_height),seed=1):image_datagen = ImageDataGenerator(**aug_dict)mask_datagen = ImageDataGenerator(**aug_dict)image_generator = image_datagen.flow_from_directory(data_path,classes=['image'],class_mode=None,color_mode=image_color_mode,target_size=target_size,batch_size=batch_size,save_to_dir=save_to_dir,save_prefix=image_save_prefix,seed=seed)mask_generator = mask_datagen.flow_from_directory(data_path,classes=['label'],class_mode=None,color_mode=mask_color_mode,target_size=target_size,batch_size=batch_size,save_to_dir=save_to_dir,save_prefix=mask_save_prefix,seed=seed)train_gen = zip(image_generator, mask_generator)for (img, mask) in train_gen:img, mask = adjust_data(img, mask)yield (img, mask)# 处理数据函数
def adjust_data(img, mask):img = img / 255mask = mask / 255mask[mask > 0.5] = 1mask[mask <= 0.5] = 0return (img, mask)smooth = 100# 定义Dice系数
def dice_coef(y_true, y_pred):y_truef = K.flatten(y_true)y_predf = K.flatten(y_pred)And = K.sum(y_truef * y_predf)return ((2 * And + smooth) / (K.sum(y_truef) + K.sum(y_predf) + smooth))# 定义损失函数
def dice_coef_loss(y_true, y_pred):return -dice_coef(y_true, y_pred)# 定义iou函数
def iou(y_true, y_pred):intersection = K.sum(y_true * y_pred)sum_ = K.sum(y_true + y_pred)jac = (intersection + smooth) / (sum_ - intersection + smooth)return jacdef jac_distance(y_true, y_pred):y_truef = K.flatten(y_true)y_predf = K.flatten(y_pred)return - iou(y_true, y_pred)model = unet()
# 打印模型参数
model.summary()#训练集图片做数据增强
train_generator_args = dict(rotation_range=0.2,width_shift_range=0.05,height_shift_range=0.05,shear_range=0.05,zoom_range=0.05,horizontal_flip=True,fill_mode='nearest')
#生成训练数据
train_gen = data_generator(train_files, BATCH_SIZE,train_generator_args,target_size=(im_height, im_width))
#生成验证数据
val_gen = data_generator(val_files, BATCH_SIZE,dict(),target_size=(im_height, im_width))checkpoint = ModelCheckpoint(filepath='./save_weights/myUnet.h5',monitor='val_acc',save_weights_only=False,save_best_only=True,mode='auto',period=1)reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=2, verbose=1)#编译模型
model.compile(optimizer=tf.keras.optimizers.Adam(1e-3), loss=dice_coef_loss, metrics=["binary_accuracy", iou, dice_coef])history = model.fit(train_gen,steps_per_epoch= train_num // BATCH_SIZE,epochs=30,validation_data = val_gen,validation_steps= val_num // BATCH_SIZE,callbacks=[checkpoint,reduce_lr])model.save_weights('./save_weights/myUnet.h5')

predict.py

from model import unet
import cv2
import os
import numpy as np
import matplotlib.pyplot as pltval_img_files = './data/val/image/'
val_label_files = './data/val/label/'images = os.listdir(val_img_files)
mask = os.listdir(val_label_files)model = unet()
model.load_weights('./save_weights/myUnet.h5')prediction_overlap = []
for i in range(20):img = cv2.imread(os.path.join(val_img_files,images[i]))img = cv2.resize(img ,(416, 416))img1 = img / 255img1 = img1[np.newaxis, :, :, :]prediction=model.predict(img1)prediction = np.squeeze(prediction)ground_truth = cv2.resize(cv2.imread(os.path.join(val_label_files,mask[i])),(416,416))ground_truth = cv2.cvtColor(ground_truth, cv2.COLOR_BGR2GRAY)ret, thresh_gt= cv2.threshold(ground_truth, 127, 255, 0)contours, hierarchy = cv2.findContours(thresh_gt, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)overlap_img = cv2.drawContours(img, contours, -1, (0, 255, 0),2)prediction[np.nonzero(prediction < 0.3)] = 0.0prediction[np.nonzero(prediction >= 0.3)] = 255.prediction = prediction.astype("uint8")ret_p, thresh_p = cv2.threshold(prediction, 127, 255, 0)contours_p, hierarchy_p = cv2.findContours(thresh_p, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)overlap_img = cv2.drawContours(img, contours_p, -1, (255,36,0),2)prediction_overlap.append(overlap_img)plt.figure(figsize=(20,20))
for i in range(len(prediction_overlap)):plt.subplot(4,5,i+1)plt.imshow(prediction_overlap[i])plt.title('Predict Image')plt.xticks([])plt.yticks([])plt.savefig("Predict.png")
plt.show()

3.结果

绿色轮廓为真实分割结果，红色轮廓为预测分割结果

肝脏分割数据集和整个项目文件下载地址：https://download.csdn.net/download/qq_38412266/12628230

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Tensorflow 2.0 医学图像分割（肝脏CT图像分割）

1.数据集

2.代码

3.结果

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