# Head-Neck Cancer Delineation
## 3 Deep Learning Methods
### Methods Review
The final layer is a 1 \(\times\) 1 convolutional layer with a sigmoid which determines the final tumor segmentation. Dice loss is used to optimize the training process. The performance of the method was evaluated using a 10-fold cross-validation strategy and three different input cases: the T1 only which obtained a DSC of 0.62, the T2 only with a DSC of 0.64, the combination T1+T2 with a DSC of 0.72. The method of [PERSON] et al. [22] proposes an automatic segmentation algorithm for 3D T1-weighted MRI, it is developed relying on a dataset of 30 patients with different cancer stages. The resolution of all the images is 528 \(\times\) 528 \(\times\) 290 and the voxel size is 0.61 \(\times\) 0.61 \(\times\) 0.8 mm\({}^{3}\). Intensity normalization is performed by using the method proposed by [PERSON] et al. [23] and then an isotropic resampling was applied to obtain a resolution of 1.0 \(\times\) 1.0 \(\times\) 1.0 mm\({}^{3}\). For the segmentation task, a CNN architecture similar to Alex-Net [24] is used, it takes image patches as input and it is composed of 5 convolutional layers and three fully connected layers. After the first, second and last layers a max-pooling layer is used to make the network learn space-invariant features. In the end, the final binary segmentation is performed by a Softmax layer. A Dropout of 0.5 is also used in the first two fully-connected layers in order to avoid overfitting. The 3D segmentation is performed in three different paths which correspond to each orthogonal perspective (axial, sagittal or coronal), then the voxel probabilities are computed averaging the probabilities of the three paths. In order to refine the segmentation, a graph cut algorithm is used to interpret the image as a graph and to solve an energy minimization problem. The network is trained for 10 epochs using 0.001 learning rate, 0.0005 weight decay, 0.9 momentum and a batch size of 100. The method is evaluated by using leave-one-subject-out cross-validation obtaining a DSC value 0.851. Finally, [PERSON] et al. [25] developed the deep learning method which obtains the best DSC score on MRI images. In this article is presented a CNN architecture based on a dataset of 29 patients from the First Affiliated Hospital, Sun Yat-Sen University and ground truth manually delineated by two experienced radiologists. A total of 87 slices of contrast-enhanced MRI (CE-MRI) is extracted, containing only the tumor area of each patient. These images are augmented to more than 60,000 slices performing rotation, changing contrast and adding random Gaussian noise. Then the images were normalized performing z-score normalization. The feature extraction phase consisted of 4 Conv-ReLu blocks and 2 Pool-Conv-Relu blocks which transform the 144 \(\times\) 144 input images into 36 \(\times\) 36 feature maps, the reconstruction phase consisted of two deconvolutional layers to obtain an output image with the original size of 144 \(\times\) 144 pixels. The network is trained with a learning rate of 10\({}^{-7}\), step size of 10\({}^{5}\), momentum of 0.9 and weight decay 5 \(\times\) 10\({}^{-4}\). The slices of 28 patients are used for training while the slices of the remaining patient were used for testing. The precision of the method measured in terms of average DSC is 0.89. In [PERSON] et al. [26] method based on CT images known as DenseUnet is described. It is a 3D network composed of a contractile path to extract the image features and an expanding path to recover the original input patch resolution. Each path is composed of dense blocks, down-sampling units and up-sampling units. Skip connections are used to assist the network to retrieve the lost information after the down-sampling process. The proposed architecture is trained and tested on a dataset containing 553 esophagus CT images from 49 distinct patients. In order to reduce memory consumption, this approach uses 3D patches rather than complete scans.