# Head-Neck Cancer Delineation
## 3 Deep Learning Methods
### Methods Review
Six sub-datasets are used in the training phase while the last one is used to test the performance of the trained network. The performance is computed in terms of DSC that reaches 0.87 for the variable patch size approach and 0.83 for the fixed one. Other methods have been developed using combinations of different image modalities. [PERSON] et al. [30] used NiftyNet [31] in order to implement V-net, a fully-convolutional neural network in 2D and 3D versions. The dataset is composed of PET/CT images of 202 patients and the relatives Gross Tumor Volumes (GTVs) manually delineated by professional radiation oncologists. This dataset is extracted from the one proposed in ([PERSON] et al. 2017) [32], and also avaible on The Cancer Imaging Archive (TCIA) in the context of radionics studies, but focusing only on 202 oropharynx tumors. The authors resampled the PET and CT volumes to an isotropic 1 \(\times\) 1 \(\times\) 1 mm voxel spacing using trilinear interpolation. Then they cropped the images to a volume of size 144 \(\times\) 144 \(\times\) 144 voxels which is centered in the oropharyngeal region, excluding other regions of little importance for this task. The architecture uses four downsampling blocks and four upsampling blocks and the final prediction is made by a residual convolutional block. The functions of downsampling and upsampling are performed by convolutional layers with 2 \(\times\) 2 \(\times\) 2 filtersusing a stride of 2. In total, there are 30 convolutional layers with ReLU activations and final softmax activation. Both 2D and 3D versions of the network are trained using an Adam optimizer with a batch size of 12, a learning rate of 0.0003 for 200 iterations. In order to evaluate the performance of these models, a leave-one-center-out cross-validation was performed. This study showed that the two modalities are complementary and there is a statistically significant improvement from 48.7% and 58.2% Dice Similarity Coefficients (DSC) with CT-only and PET-only segmentation respectively, to 60.6% with a bi-modal late fusion approach. This study shows that the 2D approach slightly outperforms the 3D one on this specific task. (60.6% vs. 59.7% respectively). The complementarity between PET and CT images is also analyzed in [PERSON] et al. [33]. this article describes a Dense-Net framework, an automatic segmentation CNN based on 3D convolution with dense connections to enable better propagation and take full advantages of the features extracted at each level from multi-modality images. This network is trained and evaluated on the dataset ([PERSON] et al. 2017) [32] of HNC from the Cancer Imaging Archive (TCIA) ([PERSON] et al. 2013) [34], focusing on 250 PET/CT images of patients with Head-Neck cancer. The dataset is split into training, validation and testing, respectively into 140, 35 and 75 patientes. The images are cropped to a 3D volume with 128 \(\times\) 128 \(\times\) 48 pixels in order to reduce memory consumption. The network structure is based on [PERSON] et al. [35] with dense blocks and transition-down and transition-up modules. The whole dense block is constructed using four convolution layers, the feature maps from all four layers are concatenated constituting the output of the dense block. This architecture contains nine dense blocks, four transition-down and four transition-up modules. Dense connection introduces an extreme connecting pattern, it links a layer to all its subsequent layers using skip connection, extremizing the concept of residual connections [36]. The quantitative accuracy of this method, measured in terms of DSC is 0.73 for the multi-modality approach, while for the PET single modality is only 0.67. [PERSON] et al. [37] develop a method based on the U-Net [16] architecture but using PET and CT images. The Gross Tumor Volumes (GTVs) are delineated by professional oncologists from 197 Head Neck Cancer (HNC) patients planned for radiotherapy at Oslo University Hospital between January 2007 and December 2013.