# Head-Neck Cancer Delineation
## 4 Other Methods
In this work, an Automatic decision Tree-based Learning Algorithm for Advanced Segmentation is applied. This algorithm was developed for previous works and for this task is applied to PET/CT scans of 20 patients with Head-Neck tumor. The ATLAAS model is able to select the most accurate method to segment a PET image, this can be made using a decision tree supervised machine learning method. This version includes two different segmentation algorithms: Adaptive Thresholding(AT) and Gaussian mixture models Clustering Method using five clusters (GCM5). The best method can be predicted on the basis of a TBR\({}_{peak}\) defined as the ratio between the tumor peak intensity value (mean value in a 1 cm\({}^{3}\) sphere centered on the maximum intensity voxel) and the background intensity (mean intensity in a 1 cm thick extension of a thresholded volume at 50% of the peak intensity value). A cut-off value is defined, and on the basis of this value, the algorithm chooses which of the 2 segmentation methods to use. In most cases, ATLAAS contours are smaller than the gold standard Gross Tumor Volume (GTV), however, the achieved average DSC among 20 patients is 0.77. A semi-automatic technique for delineating HN cancers in PET images using an enhanced random walk (RW) with automatic seed detection is described by [PERSON] et al. [49]. This study is conducted on a dataset of 18 PET images with the same resolution \(256\times 256\times 47\) and the same voxel size 2.73 \(\times\) 2.73 \(\times\) 3.27 mm\({}^{3}\). This method can automatically detect foreground/background seeds including k-means clustering which allows to make an accurate segmentation even in heterogeneous lesions. A first RW approach for segmentation of PET images was presented in [PERSON] et al. [50], in addition in this study is proposed an enhanced RW using an adaptive probability threshold for each slice, taking into account how intensity and contrast values change over the whole volume. The \(\beta\) value is set to 1 and the weights between nodes are based on SUVs following this formula:  
\[w_{ij}=exp(-\beta(SUV_{i}-SUV_{j})^{2})\]  
The algorithm can be divided into two main parts: the pre-segmentation step which detects the RW seeds and the segmentation step to delineate the cancer contours. The neighbor with a value less than 30% of the SUVmax is identified. If a voxel with a value of 30% of the \(SUV_{max}\) is found in the eight-neighborhood of a voxel containing the \(SUV_{max}\), those eight voxels are marked as background seeds. The pre-segmentation is carried out by RW using the target seed line and the eight background seeds, where the voxels with less than 50% of being foreground are rejected. The k-means algorithm automatically selects k-cluster centres following the evolution of the target in the whole volume and identifying centroids of hot regions. The identified centroids and the voxels with a SUV > 90% of \(SUV_{max}\) are identified as new target seeds, then the RW algorithm performs the segmentation using the background seeds and the target seeds. In a further extension of K-RW the probability threshold is automatically deducted by the system during the process of delineation. This method obtained a DSC of 0.848 on 40 lesions in 18 patients. [PERSON] et al. [51] developed a method that aims to delineate the target in head neck radiotherapy integrating information from three different modalities: computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI). This study is based on 22 patients with primary squamous cell carcinomas in the base of the tongue (BOT) or tonsil. The three different images for each patient are co-registered using the Velocity AI software program (Velocity Medical Systems, Atlanta, GA, USA) [52]. The segmentation algorithm is based on the Gaussian mixture modeling of the tumor region from multi-channel data, essentially it is driven by the estimation of the parameters in the Gaussian mixture model, which can be solved using the EM algorithm.