Содержание
1. Что такое UNet++?
Мощная архитектура для сегментации медицинских изображений. Эта архитектура, по сути, представляет собой сеть кодировщик-декодер с глубоким обучением, в которой подсети кодера и декодера соединены серией вложенных слоев. Переработанные слои направлены на сокращение семантического разрыва между картами признаков подсетей кодировщика и декодера.
Давайте построим модель U-Net++ на основе UNet++: A Nested U-Net Architecture for Medical Image Segmentation
Нам нужно будет провести идентификацию клеточных ядер.
Почему ядра?
Идентификация клеточных ядер является отправной точкой для большинства анализов, потому что большинство из 30 триллионов клеток в организме человека содержат ядро, содержащее ДНК генетического кода, который программирует каждую клетку. Идентификация ядра позволяет исследователям идентифицировать каждую отдельную клетку в образце. А измеряя, как клетки реагируют на различные виды лечения, исследователь может понять основные биологические процессы.
2. Зависимости
Для сборки UNet++ нам понадобятся: библиотека Tensorflow (глубокое обучение), NumPy (численные вычисления) и skimage (обработка изображений). Также мы будем использовать цветовую палитру matplotlib.
import pandas as pd import numpy as np import zipfile import os import glob import random import sys import skimage.io #Used for imshow function import skimage.transform #Used for resize function from skimage.morphology import label #Used for Run-Length-Encoding RLE to create final submission import matplotlib.pyplot as plt import tensorflow as tf from tensorflow.keras.layers import * from tensorflow.keras.models import Model from tensorflow.keras import backend as K from tensorflow.keras.callbacks import LearningRateScheduler, ModelCheckpoint, EarlyStopping, ReduceLROnPlateau from tensorflow.keras.preprocessing.image import ImageDataGenerator from sklearn.model_selection import train_test_split # Custom IoU metric def mean_iou(y_true, y_pred): prec = [] for t in np.arange(0.5, 1.0, 0.05): y_pred_ = tf.to_int32(y_pred > t) score, up_opt = tf.metrics.mean_iou(y_true, y_pred_, 2) K.get_session().run(tf.local_variables_initializer()) with tf.control_dependencies([up_opt]): score = tf.identity(score) prec.append(score) return K.mean(K.stack(prec), axis=0) IMG_WIDTH = 256 IMG_HEIGHT = 256 IMG_CHANNELS = 3 print('Python :', sys.version.split('\n')[0]) print('Numpy :', np.__version__) print('Skimage :', skimage.__version__) print('Tensorflow :', tf.__version__)3. Параметры модели
Далее мы определим некоторые конфигурации для UNet++.
# learning rate LR = 0.001 # Custom loss function def dice_coef(y_true, y_pred): smooth = 1. 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 bce_dice_loss(y_true, y_pred): return 0.5 * tf.keras.losses.binary_crossentropy(y_true, y_pred) - dice_coef(y_true, y_pred) NUM_EPOCHS=254. Загрузка изображений.
Распаковываем данные. Загружаем и объединяем изображения из path/{id}/masks/{id}.png в пустой массив. Визуализируем.
# unpack the data import zipfile for name_data in ['test', 'train']: tmp_zip = zipfile.ZipFile('../input/sf-dl-nucleus-detection/'+name_data+'.zip') tmp_zip.extractall(name_data) tmp_zip.close() # to load the data, we will use the convenient skimage lib def get_X_data(path, output_shape=(None, None)): ''' Loads images from path/{id}/images/{id}.png into a numpy array ''' img_paths = ['{0}/{1}/images/{1}.png'.format(path, id) for id in os.listdir(path)] X_data = np.array([skimage.transform.resize(skimage.io.imread(path)[:,:,:3], output_shape=output_shape, mode='constant', preserve_range=True) for path in img_paths], dtype=np.uint8) #take only 3 channels/bands return X_data def get_Y_data(path, output_shape=(None, None)): ''' Loads and concatenates images from path/{id}/masks/{id}.png into a numpy array ''' img_paths = [glob.glob('{0}/{1}/masks/*.png'.format(path, id)) for id in os.listdir(path)] Y_data = [] for i, img_masks in enumerate(img_paths): #loop through each individual nuclei for an image and combine them together masks = skimage.io.imread_collection(img_masks).concatenate() #masks.shape = (num_masks, img_height, img_width) mask = np.max(masks, axis=0) #mask.shape = (img_height, img_width) mask = skimage.transform.resize(mask, output_shape=output_shape+(1,), mode='constant', preserve_range=True) #need to add an extra dimension so mask.shape = (img_height, img_width, 1) Y_data.append(mask) Y_data = np.array(Y_data, dtype=np.bool_) return Y_data # Get training data X_train = get_X_data('train', output_shape=(IMG_HEIGHT,IMG_WIDTH)) # Get training data labels Y_train = get_Y_data('train', output_shape=(IMG_HEIGHT,IMG_WIDTH)) X_test = get_X_data('test', output_shape=(IMG_HEIGHT,IMG_WIDTH)) TRAIN_PATH = 'train/' # Check training data train_ids = next(os.walk(TRAIN_PATH)) f, axarr = plt.subplots(2,4) f.set_size_inches(20,10) ix = random.randint(0, len(train_ids[1])) axarr[0,0].imshow(X_train[ix]) axarr[0,1].imshow(np.squeeze(Y_train[ix])) axarr[0,2].imshow(X_train[ix]) axarr[0,3].imshow(np.squeeze(Y_train[ix])) axarr[1,0].imshow(X_train[ix]) axarr[1,1].imshow(np.squeeze(Y_train[ix])) axarr[1,2].imshow(X_train[ix]) axarr[1,3].imshow(np.squeeze(Y_train[ix])) plt.show()
5. Аугментация изображений.
x_train, x_test, y_train, y_test = train_test_split(X_train, Y_train, test_size=0.1, random_state=13) data_gen_args = dict(rotation_range=45., width_shift_range=0.1, height_shift_range=0.1, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, vertical_flip=True, fill_mode='reflect') X_datagen = ImageDataGenerator(**data_gen_args) Y_datagen = ImageDataGenerator(**data_gen_args) test_datagen = ImageDataGenerator() X_datagen_val = ImageDataGenerator() Y_datagen_val = ImageDataGenerator() X_datagen.fit(x_train, augment=True, seed=13) Y_datagen.fit(y_train, augment=True, seed=13) test_datagen.fit(X_test, augment=True, seed=13) X_datagen_val.fit(x_test, augment=True, seed=13) Y_datagen_val.fit(y_test, augment=True, seed=13) X_train_augmented = X_datagen.flow(x_train, batch_size=15, shuffle=True, seed=13) Y_train_augmented = Y_datagen.flow(y_train, batch_size=15, shuffle=True, seed=13) test_augmented = test_datagen.flow(X_test, shuffle=False, seed=13) X_train_augmented_val = X_datagen_val.flow(x_test, batch_size=15, shuffle=True, seed=13) Y_train_augmented_val = Y_datagen_val.flow(y_test, batch_size=15, shuffle=True, seed=13) train_generator = zip(X_train_augmented, Y_train_augmented) val_generator = zip(X_train_augmented_val, Y_train_augmented_val)6. Построение модели
Построим архитектуру модели на Tensorflow
tf.keras.backend.clear_session() nb_filter = [32,64,128,256,512] # Build U-Net++ model inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS)) s = Lambda(lambda x: x / 255) (inputs) c1 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (s) c1 = Dropout(0.5) (c1) c1 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c1) c1 = Dropout(0.5) (c1) p1 = MaxPooling2D((2, 2), strides=(2, 2)) (c1) c2 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (p1) c2 = Dropout(0.5) (c2) c2 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c2) c2 = Dropout(0.5) (c2) p2 = MaxPooling2D((2, 2), strides=(2, 2)) (c2) up1_2 = Conv2DTranspose(nb_filter[0], (2, 2), strides=(2, 2), name='up12', padding='same')(c2) conv1_2 = concatenate([up1_2, c1], name='merge12', axis=3) c3 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv1_2) c3 = Dropout(0.5) (c3) c3 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c3) c3 = Dropout(0.5) (c3) conv3_1 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (p2) conv3_1 = Dropout(0.5) (conv3_1) conv3_1 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv3_1) conv3_1 = Dropout(0.5) (conv3_1) pool3 = MaxPooling2D((2, 2), strides=(2, 2), name='pool3')(conv3_1) up2_2 = Conv2DTranspose(nb_filter[1], (2, 2), strides=(2, 2), name='up22', padding='same')(conv3_1) conv2_2 = concatenate([up2_2, c2], name='merge22', axis=3) #x10 conv2_2 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv2_2) conv2_2 = Dropout(0.5) (conv2_2) conv2_2 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv2_2) conv2_2 = Dropout(0.5) (conv2_2) up1_3 = Conv2DTranspose(nb_filter[0], (2, 2), strides=(2, 2), name='up13', padding='same')(conv2_2) conv1_3 = concatenate([up1_3, c1, c3], name='merge13', axis=3) conv1_3 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv1_3) conv1_3 = Dropout(0.5) (conv1_3) conv1_3 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv1_3) conv1_3 = Dropout(0.5) (conv1_3) conv4_1 = Conv2D(256, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (pool3) conv4_1 = Dropout(0.5) (conv4_1) conv4_1 = Conv2D(256, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv4_1) conv4_1 = Dropout(0.5) (conv4_1) pool4 = MaxPooling2D((2, 2), strides=(2, 2), name='pool4')(conv4_1) up3_2 = Conv2DTranspose(nb_filter[2], (2, 2), strides=(2, 2), name='up32', padding='same')(conv4_1) conv3_2 = concatenate([up3_2, conv3_1], name='merge32', axis=3) #x20 conv3_2 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv3_2) conv3_2 = Dropout(0.5) (conv3_2) conv3_2 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv3_2) conv3_2 = Dropout(0.5) (conv3_2) up2_3 = Conv2DTranspose(nb_filter[1], (2, 2), strides=(2, 2), name='up23', padding='same')(conv3_2) conv2_3 = concatenate([up2_3, c2, conv2_2], name='merge23', axis=3) conv2_3 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv2_3) conv2_3 = Dropout(0.5) (conv2_3) conv2_3 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv2_3) conv2_3 = Dropout(0.5) (conv2_3) up1_4 = Conv2DTranspose(nb_filter[0], (2, 2), strides=(2, 2), name='up14', padding='same')(conv2_3) conv1_4 = concatenate([up1_4, c1, c3, conv1_3], name='merge14', axis=3) conv1_4 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv1_4) conv1_4 = Dropout(0.5) (conv1_4) conv1_4 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv1_4) conv1_4 = Dropout(0.5) (conv1_4) conv5_1 = Conv2D(512, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (pool4) conv5_1 = Dropout(0.5) (conv5_1) conv5_1 = Conv2D(512, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv5_1) conv5_1 = Dropout(0.5) (conv5_1) up4_2 = Conv2DTranspose(nb_filter[3], (2, 2), strides=(2, 2), name='up42', padding='same')(conv5_1) conv4_2 = concatenate([up4_2, conv4_1], name='merge42', axis=3) #x30 conv4_2 = Conv2D(256, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv4_2) conv4_2 = Dropout(0.5) (conv4_2) conv4_2 = Conv2D(256, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv4_2) conv4_2 = Dropout(0.5) (conv4_2) up3_3 = Conv2DTranspose(nb_filter[2], (2, 2), strides=(2, 2), name='up33', padding='same')(conv4_2) conv3_3 = concatenate([up3_3, conv3_1, conv3_2], name='merge33', axis=3) conv3_3 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv3_3) conv3_3 = Dropout(0.5) (conv3_3) conv3_3 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv3_3) conv3_3 = Dropout(0.5) (conv3_3) up2_4 = Conv2DTranspose(nb_filter[1], (2, 2), strides=(2, 2), name='up24', padding='same')(conv3_3) conv2_4 = concatenate([up2_4, c2, conv2_2, conv2_3], name='merge24', axis=3) conv2_4 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv2_4) conv2_4 = Dropout(0.5) (conv2_4) conv2_4 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv2_4) conv2_4 = Dropout(0.5) (conv2_4) up1_5 = Conv2DTranspose(nb_filter[0], (2, 2), strides=(2, 2), name='up15', padding='same')(conv2_4) conv1_5 = concatenate([up1_5, c1, c3, conv1_3, conv1_4], name='merge15', axis=3) conv1_5 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv1_5) conv1_5 = Dropout(0.5) (conv1_5) conv1_5 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv1_5) conv1_5 = Dropout(0.5) (conv1_5) nestnet_output_4 = Conv2D(1, (1, 1), activation='sigmoid', kernel_initializer = 'he_normal', name='output_4', padding='same')(conv1_5) model = Model([inputs], [nestnet_output_4]) model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=LR), loss=bce_dice_loss)
7. Запуск модели
Теперь мы можем запустить модель.
checkpoint = ModelCheckpoint('best_model.hdf5' , monitor = 'val_loss', verbose = 1, save_best_only=True, mode = 'min', save_weights_only=True, save_freq='epoch' ) reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.3, patience=5, min_lr=0.00005) callbacks_list = [checkpoint, reduce_lr] # Fit model history = model.fit(train_generator, validation_data=val_generator, steps_per_epoch=len(X_train)/(6), validation_steps=10, callbacks=callbacks_list, epochs=NUM_EPOCHS, verbose=1,)
8. Визуализация результатов
Y_predict = model.predict(X_train, verbose=1) train_ids = next(os.walk('train')) test_ids = next(os.walk('test')) # Check predict data f, axarr = plt.subplots(2,3) f.set_size_inches(20,10) ix = random.randint(0, len(train_ids[1])) axarr[0,0].imshow(X_train[ix]) axarr[0,0].set_title('Microscope') axarr[0,1].imshow(np.squeeze(Y_predict[ix])) axarr[0,1].set_title('"Predicted" Masks') axarr[0,2].imshow(np.squeeze(Y_train[ix])) axarr[0,2].set_title('"GroundTruth" Masks') axarr[1,0].imshow(X_train[ix]) axarr[1,0].set_title('Microscope') axarr[1,1].imshow(np.squeeze(Y_predict[ix])) axarr[1,1].set_title('"Predicted" Masks') axarr[1,2].imshow(np.squeeze(Y_train[ix])) axarr[1,2].set_title('"GroundTruth" Masks') plt.show() # Use model to predict test labels Y_hat = model.predict(X_test, verbose=1) Y_hat.shape idx = random.randint(0, len(test_ids[1])) print(X_test[idx].shape) skimage.io.imshow(X_test[idx]) plt.show(); skimage.io.imshow(Y_hat[idx][:,:,0]) plt.show();
Смотрится неплохо.
Для удобства практического применения код доступен на Kaggle
ссылка на оригинал статьи https://habr.com/ru/post/709276/
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