# Head-Neck Cancer Delineation
## 5 Results Discussion  
After the description of the methods in the literature for this specific segmentation task, we have grouped all the results in two different tables, one for the deep learning methods and one for the others. Each row of these tables contains the name of the specific method, the dimension of the dataset in terms of the number of patients, and the Dice Similarity Coefficient (DSC) under the reference column of the modality of the image used. We chose to compare the results using the DSC as a metric because it also takes into account precision and it is present into all the considered studies. The tables have been filled with the best DSC obtained from each study, because in some studies a large experimentation is performed.  
From Tables 1 and 2 we can immediately notice inhomogeneity of datasets, some of them are composed of only a few patients and the majority of them are private. For these reasons, although some algorithms have been developed for the same image modality, it is still very difficult to perform results comparison. In general, a performance improvement can be noticed in the multi-modality approach compared to the single-modality, as it can be seen in the works of [PERSON] et al. [30], [PERSON] et al. [33], [PERSON] et al. [37] and [PERSON] et al. [51]. Nothing can be said for the remaining methods, for which no comparisons have been made between single and multi modalities. The results obtained by the analyzed methods vary in the range [0.48, 0.87] for CT images, [0.58, 0.84] for PET images, [0.64, 0.89] for MRI and [0.606, 0.78] for the multi-modality approaches. These results are comparable with those obtained by a radiologist, with the advantage that the time for delineation of the tumor is considerably shorter. In some cases, the described methods only need a few seconds for the segmentation of an image, compared to the hours taken by an expert radiologist to perform the same task. This is how the automatic segmentation methods of head and neck tumors can be considered good support to the work of radiologists. 3D U-Net is the state-of-the-art in segmentation tasks, but the only method that is 3D U-net inspired for HN cancer delineation is [PERSON] et al. [26]. The other U-net based methods ([PERSON] et al. [15], [PERSON] et al. [39] and [PERSON] et al. [37]) are applied on 2D slices. From the results shown in Table 1, it can be noticed that the best results are obtained by using multi-modal data, whereas the 2D vs. 3D approach seems to be less relevant.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**DSC**} \\ \cline{3-6}  & & **CT** & **PET** & **PET/CT** & **MRI** \\ \hline B. [PERSON] et al. [15] 2D U-Net with multi-scale & 17 patients & & & 0.644 \\   feature extraction & 17 patients & & & 0.65 \\   [PERSON] et al. [17] CNN based on DeepMedic & 33 patients & & & 0.72 \\   [PERSON] et al. [19] CNN & 44 patients & & & 0.851 \\   [PERSON] et al. [22] 3D CNN based on Alex-Net & 30 patients & & & 0.89 \\   [PERSON] et al. [25] CNN & 29 patients & & & 0.89 \\   [PERSON] et al. [26] DenseUNet & 49 patients & 0.73 & & \\   [PERSON] et al. [27] Deep Deconvolutional Neural Network & 230 patients & 0.753 & & \\   [PERSON] et al. [29] 3 paths CNN & 70 patients & 0.87 & & \\   [PERSON] et al. [30] V-net & TCIA [34] 202 patients & 0.487 & 0.582 & 0.606 \\ \hline \end{tabular}
\end{table}
Table 1: Deep learning methods.