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util_nn_bmode.py
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util_nn_bmode.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Nov 6 16:38:50 2019
@author: DUANC01
"""
from __future__ import print_function
from skimage.transform import resize
import numpy as np
from keras.models import Model
from keras.layers import Input, concatenate, Conv2D, MaxPooling2D, Conv2DTranspose
from keras.layers import Conv3D, Conv3DTranspose, MaxPooling3D
from keras.optimizers import Adam, SGD
from keras.callbacks import ModelCheckpoint
from keras import backend as K
from skimage.exposure import rescale_intensity
from keras.callbacks import History
# Some parameters
K.set_image_data_format('channels_last') # TF dimension ordering in this code
img_rows = 256
img_cols = 256
img_depths = 16
#The functions return our metric and loss
smooth = 1.
def dice_coef(y_true, y_pred):
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 dice_coef_loss(y_true, y_pred):
return -dice_coef(y_true, y_pred)
def dice_coef_square(y_true, y_pred):
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(K.square(y_true_f)) + K.sum(K.square(y_pred_f)) + smooth)
def dice_coef_loss_square(y_true, y_pred):
return -dice_coef_square(y_true, y_pred)
# Construct U-Net
def get_unet():
inputs = Input((img_rows, img_cols, 1))
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool3)
conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv5)
up6 = concatenate([Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(conv5), conv4], axis=3)
conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(up6)
conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv6)
up7 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conv6), conv3], axis=3)
conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(up7)
conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv7)
up8 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conv7), conv2], axis=3)
conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(up8)
conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv8)
up9 = concatenate([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(conv8), conv1], axis=3)
conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(up9)
conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv9)
conv10 = Conv2D(1, (1, 1), activation='sigmoid')(conv9)
model = Model(inputs=[inputs], outputs=[conv10])
model.compile(optimizer=Adam(lr=1e-4), loss=dice_coef_loss, metrics=[dice_coef])
return model
# Construct U-Net
def get_unet_deeper():
inputs = Input((img_rows, img_cols, 1))
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool3)
conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv5)
pool5 = MaxPooling2D(pool_size=(2, 2))(conv5)
conv6 = Conv2D(1024, (3, 3), activation='relu', padding='same')(pool5)
conv6 = Conv2D(1024, (3, 3), activation='relu', padding='same')(conv6)
up7 = concatenate([Conv2DTranspose(512, (2, 2), strides=(2, 2), padding='same')(conv6), conv5], axis=3)
conv7 = Conv2D(512, (3, 3), activation='relu', padding='same')(up7)
conv7 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv7)
up8 = concatenate([Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(conv7), conv4], axis=3)
conv8 = Conv2D(256, (3, 3), activation='relu', padding='same')(up8)
conv8 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv8)
up9 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conv8), conv3], axis=3)
conv9 = Conv2D(128, (3, 3), activation='relu', padding='same')(up9)
conv9 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv9)
up10 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conv9), conv2], axis=3)
conv10 = Conv2D(64, (3, 3), activation='relu', padding='same')(up10)
conv10 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv10)
up11 = concatenate([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(conv10), conv1], axis=3)
conv11 = Conv2D(32, (3, 3), activation='relu', padding='same')(up11)
conv11 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv11)
conv12 = Conv2D(1, (1, 1), activation='sigmoid')(conv11)
model = Model(inputs=[inputs], outputs=[conv12])
model.compile(optimizer=Adam(lr=1e-4), loss=dice_coef_loss, metrics=[dice_coef])
return model
def get_unet3D():
# model part
inputs = Input((img_rows, img_cols, img_depths, 1))
conv1 = Conv3D(32, (3, 3, 3), activation='relu', padding='same')(inputs)
conv1 = Conv3D(32, (3, 3, 3), activation='relu', padding='same')(conv1)
pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1)
conv2 = Conv3D(64, (3, 3, 3), activation='relu', padding='same')(pool1)
conv2 = Conv3D(64, (3, 3, 3), activation='relu', padding='same')(conv2)
pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2)
conv3 = Conv3D(128, (3, 3, 3), activation='relu', padding='same')(pool2)
conv3 = Conv3D(128, (3, 3, 3), activation='relu', padding='same')(conv3)
pool3 = MaxPooling3D(pool_size=(2, 2, 2))(conv3)
conv4 = Conv3D(256, (3, 3, 3), activation='relu', padding='same')(pool3)
conv4 = Conv3D(256, (3, 3, 3), activation='relu', padding='same')(conv4)
pool4 = MaxPooling3D(pool_size=(2, 2, 2))(conv4)
conv5 = Conv3D(512, (3, 3, 3), activation='relu', padding='same')(pool4)
conv5 = Conv3D(512, (3, 3, 3), activation='relu', padding='same')(conv5)
up6 = concatenate([Conv3DTranspose(256, (2, 2, 2), strides=(2, 2, 2), padding='same')(conv5), conv4], axis=4)
conv6 = Conv3D(256, (3, 3, 3), activation='relu', padding='same')(up6)
conv6 = Conv3D(256, (3, 3, 3), activation='relu', padding='same')(conv6)
up7 = concatenate([Conv3DTranspose(128, (2, 2, 2), strides=(2, 2, 2), padding='same')(conv6), conv3], axis=4)
conv7 = Conv3D(128, (3, 3, 3), activation='relu', padding='same')(up7)
conv7 = Conv3D(128, (3, 3, 3), activation='relu', padding='same')(conv7)
up8 = concatenate([Conv3DTranspose(64, (2, 2, 2), strides=(2, 2, 2), padding='same')(conv7), conv2], axis=4)
conv8 = Conv3D(64, (3, 3, 3), activation='relu', padding='same')(up8)
conv8 = Conv3D(64, (3, 3, 3), activation='relu', padding='same')(conv8)
up9 = concatenate([Conv3DTranspose(32, (2, 2, 2), strides=(2, 2, 2), padding='same')(conv8), conv1], axis=4)
conv9 = Conv3D(32, (3, 3, 3), activation='relu', padding='same')(up9)
conv9 = Conv3D(32, (3, 3, 3), activation='relu', padding='same')(conv9)
conv10 = Conv3D(1, (1, 1, 1), activation='sigmoid')(conv9)
model = Model(inputs=[inputs], outputs=[conv10])
model.compile(optimizer=Adam(lr=1e-4), loss=dice_coef_loss, metrics=[dice_coef])
return model
def preprocess(imgs):
imgs_p = np.ndarray((imgs.shape[0], img_rows, img_cols), dtype=np.uint8)
for i in range(imgs.shape[0]):
# need np.round here to convert 0.999 in the mask to 1, otherwise it will be zero when storing in imgs_p
imgs_p[i] = np.round(resize(imgs[i], (img_cols, img_rows), preserve_range=True))
imgs_p = imgs_p[..., np.newaxis]
return imgs_p