# Head-Neck Cancer Delineation
## 3 Deep Learning Methods
### Methods Review  
In [PERSON] et al. (2018) is presented an end-to-end algorithm for the segmentation of head and neck tumors from MRI images. A total of 163 MRI images (2D slices) from 17 patients from the Beatson west Scotland cancer center are used in this experiment. The labels are manually delineated by clinicians from Beatson. This dataset is split into three subsets, the first two are used for training and the third for the test. The proposed algorithm is a neural network similar to U-net ([PERSON] et al., 2018), with the addition of a multi-scale feature extraction process. While to access a larger receptive field there are some drawbacks like many layers or bigger kernel sizes, thanks to the dilated convolution, it is possible to look at a larger context without occurring in the reduction of feature resolution. The extracted multi-scale features are combined using concatenations to improve the performance of the network. The dataset is augmented by rotation, zoom, shift and flip in order to improve generalization, Batch normalization is also used to improve the learning process optimized using Adam optimizer. The average DSC of the cross-validation is 0.644 which is about 0.05 higher than the original U-net. [PERSON] et al. (2018) analyzes the contribution of 7 MRI input channels in a CNN for the segmentation of Head Neck cancer. The dataset of this work contains a total of 33 MRI of patients with head-neck cancer (HNC), the Gross Tumor Volume (GTV) delineation is performed by expert radiation oncologists and radiologists mainly on T1- and T2-weighted images. All the images are co-registered and interpolated to a common base resolution of 0.45 \(\times\) 0.45 \(\times\) 2 mm\({}^{3}\) and normalized to a standard deviation and mean of 0.25 each. The described CNN is based on the DeepMedic architecture ([PERSON] et al., 2018), with the use of two pathways, one with the original resolution and the other subsampled of a factor of 3. The network is composed of 10 convolutional layers each with 104 feature maps and residual connections in layers 4, 6, 8 and 10. A 20% dropout is set in each layer and the Dice loss function is used to train the network. This system uses a patch-based approach, the patch size is 38 \(\times\) 38 \(\times\) 8 and 78 \(\times\) 78 \(\times\) 8 pixels respectively for each path. The CNN is trained with all seven input channels and also in other seven configurations in which one of the input channels is left out. Trying all the different configurations allows understanding which of the seven inputs carries a greater impact in this task. The final segmentation performance is evaluated with the DSC whose value reaches 0.65. Another study made by [PERSON] et al. (2019), proposes an automated segmentation method for nasopharyngeal carcinoma based on convolutional neural network (CNN) on a dataset containing 44 T1- and T2-weighted MRI images for a total of 1950 pairs of image slices. A couple of images of each patient are co-registered in order to make use of the information of both the modalities. The images are resampled by using linear interpolation, min-max normalization is performed and all the slides of the images are zero padded and cropped to 256 \(\times\) 256 pixels. The network is divided into an encoder part and a decoder part: the encoder extracts the features of the images thanks to four encoder blocks and a dense connectivity block, the decoder uses deconvolutions to recover the extracted features to the initial input size. The encoder is composed of three 3 \(\times\) 3 convolutional layers, two group normalization layers ([PERSON] et al., 2018) followed by leaky rectified linear unit (LReLU) layers ([PERSON] et al., 2018). The decoder consists of five decoder blocks, each block is composed of a 3 \(\times\) 3 deconvolutional layer, a concatenation layer and two 3 \(\times\) 3 convolutional layers followedby two LReLU and GN layers. Thanks to the concatenation layer, the feature maps are fused to address the problem of losing information after the deconvolutions.