Evaluation Exploration of Atlas-Based and Deep Learning-Based Automatic Contouring for Nasopharyngeal Carcinoma

Abstract

PurposeThe purpose of this study was to evaluate and explore the difference between an atlas-based and deep learning (DL)-based auto-segmentation scheme for organs at risk (OARs) of nasopharyngeal carcinoma cases to provide valuable help for clinical practice.Methods120 nasopharyngeal carcinoma cases were established in the MIM Maestro (atlas) database and trained by a DL-based model (AccuContour®), and another 20 nasopharyngeal carcinoma cases were randomly selected outside the atlas database. The experienced physicians contoured 14 OARs from 20 patients based on the published consensus guidelines, and these were defined as the reference volumes (Vref). Meanwhile, these OARs were auto-contoured using an atlas-based model, a pre-built DL-based model, and an on-site trained DL-based model. These volumes were named Vatlas, VDL-pre-built, and VDL-trained, respectively. The similarities between Vatlas, VDL-pre-built, VDL-trained, and Vref were assessed using the Dice similarity coefficient (DSC), Jaccard coefficient (JAC), maximum Hausdorff distance (HDmax), and deviation of centroid (DC) methods. A one-way ANOVA test was carried out to show the differences (between each two of them).ResultsThe results of the three methods were almost similar for the brainstem and eyes. For inner ears and temporomandibular joints, the results of the pre-built DL-based model are the worst, as well as the results of atlas-based auto-segmentation for the lens. For the segmentation of optic nerves, the trained DL-based model shows the best performance (p < 0.05). For the contouring of the oral cavity, the DSC value of VDL-pre-built is the smallest, and VDL-trained is the most significant (p < 0.05). For the parotid glands, the DSC of Vatlas is the minimum (about 0.80 or so), and VDL-pre-built and VDL-trained are slightly larger (about 0.82 or so). In addition to the oral cavity, parotid glands, and the brainstem, the maximum Hausdorff distances of the other organs are below 0.5 cm using the trained DL-based segmentation model. The trained DL-based segmentation method behaves well in the contouring of all the organs that the maximum average deviation of the centroid is no more than 0.3 cm.ConclusionThe trained DL-based segmentation performs significantly better than atlas-based segmentation for nasopharyngeal carcinoma, especially for the OARs with small volumes. Although some delineation results still need further modification, auto-segmentation methods improve the work efficiency and provide a level of help for clinical work.

Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring for Head and Neck Radiation Therapy

“…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.…”

“…Various commercial auto-contouring solutions have emerged over past few years to address these issues. Atlas-based and deep learning (DL) approaches are 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.…”

“…Over the past five years, use of DL in medical imaging has become popular [13][14][15]. Commercial companies such as Manteia 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].…”

“…This results in the greatest inter-and intraoperator 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 (Manteia Medical Technologies Co., Xiamen, China) [5], Maestro 6.9.6 versus INTContour (Carina Medical LLC, Lexington, KY, USA) [6], ANAtomically Constrained Deformation Algorithm (ANA-CONDA) (RaySearch Laboratories AB, Stockholm, Sweden) versus INTContour [10], and WorkflowBox 1.4 (Mirada Medical Limited, Oxford, UK) versus Mirada Medical Limited DLCExpert [27].…”