论文3D|论文3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes解读论文3DDeeplySupervisedNetworkforAut

论文地址：https://arxiv.org/abs/1607.00582
这是一篇MICCAI 2016关于肝脏分割的论文，使用了3D卷积神经网络，难点是虚线里面的部分，如何体现出三个输出的监督作用，最初感觉是在损失函数里面体现出来，尝试把损失函数的一个预测变量改成三个，但运行网络总是报错，后来反复理解这三个输出如何起作用，以及下图中三个输出和label之间的虚线，搜索了与多输出有关的技术博客，最后完全理解了这个网络结构的运行过程，具体到代码的差异性如下红框所示：

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模型结构图.png
模型结构在下采样的过程中有三个输出分支，使用Deconvolution来进行上采样，模型结构比较容易理解，主要是代码实现过程中和平时有一点差异，下图红框是需要注意的地方：

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Keras代码.png
整个网络架构的代码如下：

def DSN(self):inputs = Input((32,self.img_rows, self.img_cols,1))conv1 = Conv3D(8, (7, 9, 9), padding='same', activation= 'selu', kernel_initializer = 'he_normal')(inputs) conv1 = Conv3D(8, (7, 9, 9), padding='same',activation= 'selu',kernel_initializer = 'he_normal')(conv1) print("conv1 shape:", conv1.shape) pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1) print("pool1 shape:", pool1.shape) convT1 = Conv3DTranspose(2, (2, 2, 2), padding='valid', strides = (2, 2, 2), activation= 'selu', kernel_initializer = 'he_normal')(pool1) print("convT1 shape:", convT1.shape) out1 = Conv3D(1, 1, activation = 'softmax')(convT1)conv2 = Conv3D(16, (5, 7, 7), padding='same', activation= 'selu', kernel_initializer = 'he_normal')(pool1) conv2 = Conv3D(32, (5, 7, 7), padding='same',activation= 'selu',kernel_initializer = 'he_normal')(conv2) print("conv2 shape:", conv2.shape) pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2) print("pool2 shape:", pool2.shape) convT2 = Conv3DTranspose(2, (2, 2, 2), padding='valid', strides = (2, 2, 2), activation= 'selu', kernel_initializer = 'he_normal')(pool2) convT2 = Conv3DTranspose(2, (2, 2, 2), padding='valid', strides = (2, 2, 2), activation= 'selu', kernel_initializer = 'he_normal')(convT2) print("convT2 shape:", convT2.shape) out2 = Conv3D(1, 1, activation = 'softmax')(convT2)conv3 = Conv3D(32, (3, 5, 5), padding='same', activation= 'selu', kernel_initializer = 'he_normal')(pool2) conv3 = Conv3D(32, (1, 1, 1), padding='same',activation= 'selu',kernel_initializer = 'he_normal')(conv3) print("conv3 shape:", conv3.shape) convT3 = Conv3DTranspose(2, (2, 2, 2), padding='valid', strides = (2, 2, 2), activation= 'selu', kernel_initializer = 'he_normal')(conv3) convT3 = Conv3DTranspose(2, (2, 2, 2), padding='valid', strides = (2, 2, 2), activation= 'selu', kernel_initializer = 'he_normal')(convT3) print("convT3 shape:", convT3.shape) out3 = Conv3D(1, 1, activation = 'sigmoid')(convT3)model = Model(input=inputs, output=[out3, out2, out1]) adam = Adam(lr=0.0001) model.summary()model.compile(optimizer=adam, loss=self.dice_coef_loss, loss_weights=[0.6, 0.3, 0.1]) with open('seg_liver3D.json', 'w') as files: files.write(model.to_json()) print('model compile') return modeldef train(self): print("loading data") imgs_train, label_train = self.load_train_data() print("loading data done") model = self.get_unet() print("got unet")# 保存的是模型和权重, model_checkpoint = ModelCheckpoint('seg_liver3D.h5', monitor='loss', verbose=0, save_best_only=True, save_weights_only=True, mode='min') print('Fitting model...') model.fit(imgs_train, [label_train, label_train, label_train] ,batch_size=2, epochs=15, verbose=1, callbacks=[model_checkpoint], validation_split=0.2, shuffle=True)

【论文3D|论文3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes解读】网络架构中使用转置卷积来进行上采样过程。