# Head-Neck Cancer Delineation
## 3 Deep Learning Methods
### Methods Review
The dataset contains 197 Head Neck Cancer (HNC) patients planned for radiotherapy at Oslo University Hospital between January 2007 and December 2013. It is split into three parts: 142 patients for the training phase, 15 for the validation phase and 40 patients for testing. Then PET and CT images are co-registered and the model is trained for both single (CT-only and PET-only) and bi-modal (PET/CT) approaches. In order to train the U-Net Adam optimizer is used with a learning rate of \(10^{-4}\) and standard parameters. After each activation function, batch normalization is used to make the training process more stable. The method is evaluated with both Cross-Entropy and Dice loss functions. The validation set is useful to explore the hyperparameters configuration that provided the best precision in terms of the Dice similarity coefficient. Then, the generalization ability of the algorithm is tested on the 40 patients of the test set. The results in terms of Dice coefficient are the following: CT: 0.65 \(\pm\) 0.17, PET: 0.71 \(\pm\) 0.12, PET/CT: 0.75 \(\pm\) 0.12  
[PERSON] et al. [38] aim to exploit the complementary information within PET and CT imaging spaces. For this purpose, a two-stream chained 3D deep network fusion pipeline is designed. This network uses early and late 3D deep network fusions of CT and PET images. The images are co-registered applying the cubic B-spline and using the lung mass centers of the CT scans as initial matching position. The mass center is produced by the P-HNN model, which generates a segmentation of the lung field even in severely pathological cases, generating aligned PET/CT images. First, two separate streams generate segmentation maps using the CT images and the early fusion PET/CT. The functioning of the pipeline can be seen as a late fusion of the CT and early fusion models. To effect the fusion and segmentation, a progressive semantically nested network (PSNN) is proposed. It consists of a set of 1 \(\times\) 1 \(\times\) 1 3D convolutional layers which collapse the feature maps after each convolutional block into a logit image. Then, this image is combined with the image previously generated by the higher level segmentation and finally, an aggregated segmentation map is created. The PSNN is trained using four deeply-supervised auxiliary losses at each convolution block. For the implementation, the Adam optimizer is used with a momentum of 0.99 and a weight decay of 0.005 for a total of 40 epochs. The method is trained on a dataset of 110 esophageal cancer patients using 5-fold cross-validation for the evaluation. The experiment demonstrates that both the two-stream chained pipeline and the PSNN obtained really good results, with a DSC of 76.4%. We know the importance of generalization in these methods, this means that a segmentation method should also be able to perform on images from different diagnostic centers. [PERSON] et al. (2018) propose a method based on PET/CT images collected from two different diagnostic centers, the first one contains 17 patients and the second one 5 patients for a total of 22 Head-neck cancer (HNC) patients. The Gross Tumor Volume (GTV) delineation is manually made by an oncologist and a radiologist and used as the gold standard for the training phase. A Deep Convolutional Neural Network (DCNN) model inspired by the fully convolutional network and U-net is designed. As in the U-net architecture, the process consists in two stages: feature representation phase and scores map reconstruction phase. The feature representation consists in the extraction of the feature information of PET/CT images, combining the low-level information and representing a high-level feature with semantic information. This process is carried out by five downsampling blocks, four convolutional layers with ReLu as the activation function. The reconstruction phase consists of five upsampling blocks, a convolutional layer with ReLu as the activation function.