# Head-Neck Cancer Delineation
###### Abstract  
Head-Neck Cancer (HNC) has a relevant impact on the oncology patient population and for this reason, the present review is dedicated to this type of neoplastic disease. In particular, a collection of methods aimed at tumor delineation is presented, because this is a fundamental task to perform efficient radiotherapy. Such a segmentation task is often performed on uni-modal data (usually Positron Emission Tomography (PET)) even though multi-modal images are preferred (PET-Computerized Tomography (CT)/PET-Magnetic Resonance (MR)). Datasets can be private or freely provided by online repositories on the web. The adopted techniques can belong to the well-known image processing/computer-vision algorithms or the newest deep learning/artificial intelligence approaches. All these aspects are analyzed in the present review and comparison among various approaches is performed. From the present review, the authors draw the conclusion that despite the encouraging results of computerized approaches, their performance is far from handmade tumor delineation result.  
head-neck cancer (HNC); head and neck squamous cell carcinoma (HNSCC); nasopharyngeal cancer (NPC); segmentation; tumor delineation; CT; PET; MRI +
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appropriate contrast medium must be used to visualize particular parts of the human body. Despite a high spatial resolution, the contrast of such images is low for the HNC. For this reason, often the methods make use of multi-modal data (usually PET-CT) to compensate for the respective lack of image features (spatial resolution for PET, low contrast for CT). Often these images are merged to create a new image that is used for tumor delineation. Recently, Magnetic Resonance (MR) scans have been considered for the HNC. MR Imaging (MRI) makes use of an intense magnetic field and electromagnetic waves to obtain detailed images of the organs and tissues of the human body. Like a CT scan, it provides a cross-sectional view of the human body and can produce images with different contrast among the tissues in the function of the type of electromagnetic waves (T1-weighted, T2-weighted and so on). Even if it exhibits a good spatial resolution and the contrast among tissues is better than CT, it is not sufficient to distinguish the neoplastic tissue from the surrounding one, so also in this case MR is used in combination with PET [7,8]. The usefulness of using PET and MRI combined together in the evaluation of head and neck cancers is also investigated in the study of [PERSON] et al. [9]. In the end, the delineation of tumors is a task making use of multi-modality imaging but, when it is manually performed, it incurs several problems: it is time-consuming, labour-intensive and prone to inter- and intra-observer variations [10,11]. In Figure 1 the comparison among PET, CT and MR images are shown. As a consequence, the manual inconsistency affects the result of a hand-made segmentation and for the same reason the gold standard is affected by the same issues, because it is often obtained by manual segmentation. Finding an automatic/semi-automatic, robust and precise method to delineate the neoplastic formation is an important field of research to overcome the issues mentioned above for the manual segmentation. The present review collects papers regarding HNC considering all the diagnostic modalities and methods adopted to perform an automatic and semi-automatic segmentation. The paper is organized as follows: Section 2 describes the methodology adopted to collect the papers, Section 3 describes the deep learning methods, Section 4 reports all the other methods, Section 5 is dedicated to the comparison between these two approaches, Section 6 reports some conclusions.