cell_fcrna.py

#implementation of model based on 'Microscopy Cell Counting with Fully Convolutional Regression Networks' by Xie et al. 

from keras.layers import Input
from keras.layers.core import Dropout, Lambda
from keras.layers.convolutional import Conv2D, Conv2DTranspose, UpSampling2D
from keras.layers.pooling import MaxPooling2D
from keras.layers.merge import concatenate

class FCRN_A_Class:
	def __init__(self, IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS, IMG_CLASSES):
		#build the keras model of unet
		#source: U-Net: Convolutional Networks for Biomedical Image Segmentation, by Ronneberger et al
		#make a tensor for the inputs
		self.inputs = Input((IMG_HEIGHT,IMG_WIDTH,IMG_CHANNELS))

		#keras defines layers of a model, such as lambda, conv2d, etc..
		s = Lambda(lambda x: x/255)(self.inputs) #128x128x1

		#conv2d(filters, kernel_size,options)

		c1 = Conv2D(32,(3,3),activation='relu',kernel_initializer='Orthogonal',padding='same')(s) #9 weights * 1 channel * 32 features + 1 bias * 32 features = 320
		p1 = MaxPooling2D((2,2))(c1)

		c2 = Conv2D(64,(3,3),activation='relu', kernel_initializer='Orthogonal', padding='same') (p1)#9 * 32 * 64 + 1 * 64 = 18496 parameters
		p2 = MaxPooling2D((2, 2)) (c2)

		c3 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='Orthogonal', padding='same') (p2)
		p3 = MaxPooling2D((2, 2)) (c3)

		fc = Conv2D(512, (3, 3), kernel_initializer='Orthogonal', padding='same') (p3)#16x16x512
		
		u4 = UpSampling2D(size=(2,2))(fc)
		c4 = Conv2DTranspose(128, (3, 3), activation='relu', kernel_initializer='Orthogonal', padding='same') (u4)

		u5 = UpSampling2D(size=(2,2))(c4)
		c5 = Conv2DTranspose(64, (3, 3), activation='relu', kernel_initializer='Orthogonal', padding='same') (u5)

		u6 = UpSampling2D(size=(2,2))(c5)
		c6 = Conv2DTranspose(32, (3, 3), activation='relu', kernel_initializer='Orthogonal', padding='same') (u6)

		self.outputs = Conv2D(IMG_CLASSES, (3, 3), activation='relu', kernel_initializer='Orthogonal', padding='same') (c6)