# Head-Neck Cancer Delineation
## 3 Deep Learning Methods
### Methods Review
The main component of the network is the dense block, it contains one conv(1 \(\times\) 1 \(\times\) 1)-BN-ReLU and one conv(3 \(\times\) 3 \(\times\) 3)-BN-ReLU. The down-sampling block is a conv(1 \(\times\) 1 \(\times\) 1)-BN-ReLU-MaxPool and the up-sampling conv(3 \(\times\) 3 \(\times\) 3)-BN-ReLU-deconv(3 \(\times\) 3 \(\times\) 3). The final part of the network consists in another convolutional layer and a soft-max layer in order to compute the output which can be classified as Gross Tumor Volume (GTV) or background. The network is trained for 10k iterations with a batch size of 20, for the optimization is used the Adam optimizer with a learning rate of 10\({}^{-4}\). The dataset containing 553 scans from 49 distinct patients is split into 30 patients for training, 6 for validation and 13 for testing. During every iteration, data augmentation is performed adding withe noise to the input patches. Different tests are done varying the number of sub-blocks and also the number of feature maps inside each sub-block and are computed the DSC for each configuration to compare them and to find which one is better. The best configuration of the network was DenseUnet\({}_{122}\) which achieves a DSC value of 0.73. Remaining within the scope of CT images [PERSON] et al. [27] develops a Deep Deconvolutional Neural Network (DDNN) consisting of two components: an encoder and a decoder network. The encoder is made of 13 convolutional layers and it is intended to extract feature maps of the input images, then the decoder will recover the original resolution of the images by deploying deconvolution. The encoder layers are based on the VGG-16 architecture [28], which is well-known for feature extraction. In addition, for this task, the fully connected layers were replaced with fully convolutional layers. The dataset used in this work consists of 230 CT images of patients diagnosed with Nasopharyngeal Cancer (NPC). Radiation oncologists contoured the nasopharynx gross tumor volume (GTVnx), the metastatic lymph node gross tumor volume (GTVnd), clinical target volume (CTV), and organs at risk (OARs) in the planning CT. Images from 184 patients were randomly chosen to be part of the training set and the remaining six patients were used to evaluate the performance of the model. The initial learning rate was set to 0.0001, learning rate decay factor to 0.0005 and decay step size to 2000. The network is trained until the precision on the training set converged and then it was tested on the validation set. The DSC values how this method outperformed the VGG-16, obtaining an average score of 75.3% \(\pm\) 11.3% compared to 59.9% \(\pm\) 22.7% of the VGG-16. The work of [PERSON] et al. [29] proposes a deep-learning method for the nasopharynxeal carcinoma (NPC) segmentation from CT images. The dataset consists of 70 CT images of patients with NPC, for each patient all the contours of cancer are traced from two radiation oncologists. The method includes two phases: during the first phase, the non-target organ regions are eliminated from CT images, during the second phase the NPC is detected from the remaining part of the images after the first step. The system is composed of three different paths for each section (axial,coronal and sagittal) for a total of six CNNs: three for the detection of non-target organ region and three for the detection of NPC. After the whole process, the output of the second path is integrated into a single image containing the segmented tumor. The CNNs are composed of three convolutional layers with max-pooling and ReLu activation function, and one fully connected layer. All the convolutional layers use filters with a size of 3 \(\times\) 3 and a stride of 1 with the same padding. We developed two different systems, the first uses fixed size patch size and the second variable patch size as input images. To evaluate the applicability of the proposed systems, the dataset is divided into seven sub-datasets, each of which contains 10 patients.