# Head-Neck Cancer Delineation
## 6 Conclusions and Future Works  
This review is born with the aim of comparing different methods for the segmentation of head-neck tumors by exploiting the characteristics and information that can be extrapolated from different medical image modalities. Segmentation is an important task both in decision-making like in [PERSON] et al. [53], for other systems aimed to medical diagnosis and in second opinion systems ([PERSON] et al. [54,55]). Methods for PET, CT and MR images were compared, highlighting how the three image modalities can be complementary to each other. In addition, we can certainly say that computer-assisted segmentation can help physicians in performing this task. Anyway, the computer-assisted or completely automatic methods can not be comparable with hand-made tumor delineation because these systems reach an average DSC percentage of 74% for deep learning methods and 75% for the other methods. The best results and the gold standard are obtained by handmade segmentation. Another important criticism regarding these methods is the fact that almost the entire of these studies are conducted on private datasets and many of them are composed of few patients. The fact that many of these datasets are private (except The Cancer Imaging Archive TCIA [34]) makes the results not comparable with each other, even in the case of images of the same type (CT or PET or MR). In addition, the fact that many of them are composed of a small number of patients means that it is not possible to demonstrate the ability to generalize these methods, which could work well for that specific dataset but they could not work if they would be tested on other datasets. Since the images often come from only one center (except in some cases), the algorithms have been tested on uniform images with regard to resolution and voxel size and acquisition protocol. This also does not guarantee the ability to generalize the methods presented in the literature. Therefore, it is possible to conclude by saying that automatically segmenting the tumors of the head-neck area is possible and even with discrete results, but it would be useful to have a universal opportunity to compare the various methods, perhaps on a public dataset containing many images. However, taking into consideration the present review, there is \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Dataset**} & \multicolumn{5}{c}{**DSC**} \\ \cline{3-6}  & & **CT** & **PET** & **PET/CT** & **MRI** \\ \hline [PERSON] et al. [33] 3D Dense-Net & TCIA [34] 250 patients & & 0.67 & 0.72 & \\ [PERSON] et al. [37] U-Net based CNN & 142 patients & 0.65 & 0.71 & 0.75 & \\ [PERSON] et al. [38] two-stream chained & 110 patients & & & 0.764 & \\
3D deep network fusion pipeline & 110 patients & & & & \\ [PERSON] et al. [39] 2D U-Net & 22 patients & & & 0.785 & \\ [PERSON] et al. [40] Combined CNN (C-CNN) & 90 patients & 0.746 & & & \\ \hline  & **Average** & 0.706 & 0.654 & 0.725 & 0.751 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Dataset**} & \multicolumn{5}{c}{**DSC**} \\ \cline{3-5}  & & **PET** & **MRI** & **PET/CT/MRI** \\ \hline B. [PERSON] et al. [43] Fuzzy & & 50 patients & & 0.7 & \\ C-Mean and 3D LSM & & & & \\ [PERSON] et al. [44] 3D active surface & 2 pet + 2 phantom = 4 patients & \(\sim\)0.6 & & \\ [PERSON] et al. [48] ATLAAS & 20 patients & 0.77 & & \\ [PERSON] et al. [49] Random Walk & 18 patients & 0.848 & & \\ [PERSON] et al. [51] & 22 patients & 0.65 & & 0.74 \\ Gaussian mixture modeling & & & & \\ \hline  & **Average** & 0.717 & 0.73 & 0.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Other methods. still extensive room for improvement in this field of research. In the future, the authors are planning to implement a CNN to segment PET-CT volumes regarding patients affected by HN cancer.