Abstract
Purpose: This study was conducted to develop a deep learning-based automatic segmentation (DLBAS) model of head and neck organs for radiotherapy (RT) in dogs, and to evaluate the feasibility for delineating the RT planning.Materials and Methods: The segmentation indicated that there were potentially 15 organs at risk (OARs) in the head and neck of dogs. Post-contrast computed tomography (CT) was performed in 90 dogs. The training and validation sets comprised 80 CT data sets, including 20 test sets. The accuracy of the segmentation was assessed using both the Dice similarity coefficient (DSC) and the Hausdorff distance (HD), and by referencing the expert contours as the ground truth. An additional 10 clinical test sets with relatively large displacement or deformation of organs were selected for verification in cancer patients. To evaluate the applicability in cancer patients, and the impact of expert intervention, three methods–HA, DLBAS, and the readjustment of the predicted data obtained via the DLBAS of the clinical test sets (HA_DLBAS)–were compared.Results: The DLBAS model (in the 20 test sets) showed reliable DSC and HD values; it also had a short contouring time of ~3 s. The average (mean ± standard deviation) DSC (0.83 ± 0.04) and HD (2.71 ± 1.01 mm) values were similar to those of previous human studies. The DLBAS was highly accurate and had no large displacement of head and neck organs. However, the DLBAS in the 10 clinical test sets showed lower DSC (0.78 ± 0.11) and higher HD (4.30 ± 3.69 mm) values than those of the test sets. The HA_DLBAS was comparable to both the HA (DSC: 0.85 ± 0.06 and HD: 2.74 ± 1.18 mm) and DLBAS presented better comparison metrics and decreased statistical deviations (DSC: 0.94 ± 0.03 and HD: 2.30 ± 0.41 mm). In addition, the contouring time of HA_DLBAS (30 min) was less than that of HA (80 min).Conclusion: In conclusion, HA_DLBAS method and the proposed DLBAS was highly consistent and robust in its performance. Thus, DLBAS has great potential as a single or supportive tool to the key process in RT planning.
Highlights
Radiation therapy (RT) is one of the methods for cancer treatment that utilizes beams of intense energy to eliminate cancer cells
Several procedures are used in RT, and organ segmentation is a prerequisite for quantitative analysis and RT planning [4]
Organ segmentation is achieved by delineating along the boundaries of the organs at risk (OARs) and clinical target volumes (CTVs)
Summary
Radiation therapy (RT) is one of the methods for cancer treatment that utilizes beams of intense energy to eliminate cancer cells. The use of RT in clinical practice has evolved over a long period [1] Veterinary facilities are both small in size and number when compared to that of human medicine facilities. Segmentations are manually achieved by experts during RT planning, especially, the threedimensional conformal and intensity-modulated RT, as they require more accurate delineation of the CTVs and OARs [3, 6]. Delineation is challenging and time-consuming owing to the complexity of the structures involved This procedure requires considerable attention to detail and expertise in anatomy and imaging modality. Practitioners in human medicine have overcome these limitations by using auto-segmentation techniques, which have gained significant attention for their potential use in routine clinical workflows [3]. The current main research focus of RT is deep-learning-based auto-segmentation (DLBAS); this is the most recent method for automatic segmentation [3, 10,11,12,13,14,15,16,17,18,19,20,21]
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