def unet_model_xd3_2_6l_grid(nb_filter=48, dim=5, clen=3 , img_rows=224, img_cols=224 ): # NOTE that this procedure is/should be used with img_rows & img_cols as None
# aiming for architecture similar to the http://cs231n.stanford.edu/reports2016/317_Report.pdf
# Our model is six layers deep, consisting of a series of three CONV-RELU-POOL layyers (with 32, 32, and 64 3x3 filters), a CONV-RELU layer (with 128 3x3 filters), three UPSCALE-CONV-RELU lay- ers (with 64, 32, and 32 3x3 filters), and a final 1x1 CONV- SIGMOID layer to output pixel-level predictions. Its struc- ture resembles Figure 2, though with the number of pixels, filters, and levels as described here
## 3D CNN version of a previously developed unet_model_xd_6j
zconv = clen
inputs = Input((1, dim, img_rows, img_cols))
conv1 = Convolution3D(nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(inputs)
conv1 = Convolution3D(nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv1)
pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1)
conv2 = Convolution3D(2*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(pool1)
conv2 = Convolution3D(2*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv2)
pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2)
conv4 = Convolution3D(4*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(pool2)
conv4 = Convolution3D(4*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv4)
up6 = merge([UpSampling3D(size=(2, 2, 2))(conv4), conv2], mode='concat', concat_axis=1)
conv6 = Convolution3D(2*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(up6)
conv6 = Convolution3D(2*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv6)
up7 = merge([UpSampling3D(size=(2, 2, 2))(conv6), conv1], mode='concat', concat_axis=1) # original - only works for even dim
conv7 = Convolution3D(nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(up7)
conv7 = Convolution3D(nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv7)
pool11 = MaxPooling3D(pool_size=(2, 1, 1))(conv7)
conv12 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(pool11)
conv12 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(conv12)
pool12 = MaxPooling3D(pool_size=(2, 1, 1))(conv12)
conv13 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(pool12)
conv13 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(conv13)
pool13 = MaxPooling3D(pool_size=(2, 1, 1))(conv13)
if (dim < 16):
conv8 = Convolution3D(1, 1, 1, 1, activation='sigmoid')(pool13)
else: # need one extra layer to get to 1D x 2D mask ...
conv14 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(pool13)
conv14 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(conv14)
pool14 = MaxPooling3D(pool_size=(2, 1, 1))(conv14)
conv8 = Convolution3D(1, 1, 1, 1, activation='sigmoid')(pool14)
model = Model(input=inputs, output=conv8)
model.compile(optimizer=Adam(lr=1e-4), loss=dice_coef_loss, metrics=[dice_coef])
#model.compile(optimizer=Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0), loss=dice_coef_loss, metrics=[dice_coef])
return model
评论列表
文章目录
