## 1 Introduction  
Nowadays, intensity-modulated radiation therapy (IMRT) is an important cancer treatment option as a result of its capability of reduction of toxic effects associated with radiation therapy (RT). One of the essential requirements for IMRT is anatomy contouring [1; 2; 3]. Traditionally, this requires radiation therapists and/or oncologists to manually identify and contour tumors (clinical target volumes (CTVs)) and normal tissues (organs at risk (OARs)). Labor-intensiveness, and inter- and intra-operator variabilities are two major issues of the manual contouring [1; 2; 3; 4; 5]. Various commercial auto-contouring solutions have emerged over past few years to address these issues. Atlas-based and deep learning (DL) approachesare used to develop these auto-contouring solutions [3; 4; 5; 6; 7; 8; 9; 10; 11]. The atlas-based method involves automatically registering reference patient contours (gold standard/ground truth) to new patients through deforming reference patient contours for matching new patient anatomical structures. This approach can be subdivided into three categories, namely single atlas (with use of one reference dataset), multi-atlas (using multiple datasets) and hybrid (based on outcome of statistical analysis of multiple gold standards) [3; 4; 12]. Over the past five years, use of DL in medical imaging has become popular [13; 14; 15]. Commercial companies such as Mantea Medical Technologies Co. (Xiamen, China) [5], Mirada Medical Limited (Oxford, UK) [7] and Carina Medical LLC (Lexington, KY, USA) [10] have applied the widely used deep convolutional neural network (DCNN) architecture to develop their auto-contouring models (software). The DL auto-contouring model development involves providing training datasets with ground truth contours to the model for learning features of tumors and normal tissues (including those not known by human) automatically. With sufficient training, the model becomes capable to automatically search for these features and locate them to achieve auto-contouring for new patients. Hence, it is believed that the DL auto-contouring approach performs better than the atlas-based method because of its capability of identifying unknown but relevant features for more accurate contouring [1; 2; 3; 5; 7; 10; 11; 12; 16; 17; 18; 19; 20; 21]. It is well known that head and neck cancer segmentation is a challenging task because computed tomography (CT) images which have limited soft tissue contrast are commonly used in the RT planning process [1; 2; 22; 23; 24; 25; 26]. This results in the greatest inter- and intra-operator variabilities and takes about 2-3 h for the manual contouring in the head and neck RT [2; 22; 23; 24; 25]. Few studies compared performances between commercial atlas-based and DL auto-contouring software packages with the manual contouring as the reference [5; 6; 10; 27]. The investigated atlas-based and DL auto-contouring software pairs included Maestro 6.6.5 (MIM Software Inc., Cleveland, OH, USA) versus AccuContour (Mantea Medical Technologies Co., Xiamen, China) [5], Maestro 6.9.6 versus INTContour (Carina Medical LLC, Lexington, KY, USA) [6], ANAtomically Constrained Deformation Algorithm (ANACONDA) (RaySearch Laboratories AB, Stockholm, Sweden) versus INTContour [10], and WorkflowBox 1.4 (Mirada Medical Limited, Oxford, UK) versus Mirada Medical Limited DLCExpert [27]. Those studies showed consistent results that the DL auto-contouring was more accurate and required less time for segmentation of OARs [5; 6; 10; 27]. Given these promising results of the DL auto-contouring, it appears worthwhile to further investigate the potential of other unexplored DL auto-contouring software packages such as RaySearch Laboratories AB RayStation 12A RSL Head and Neck CT 2.0.0.47 DL auto-contouring model.