RESUMO
PURPOSE: This study addressed the challenge of detecting and classifying the severity of ductopenia in parotid glands, a structural abnormality characterized by a reduced number of salivary ducts, previously shown to be associated with salivary gland impairment. The aim of the study was to develop an automatic algorithm designed to improve diagnostic accuracy and efficiency in analyzing ductopenic parotid glands using sialo cone-beam CT (sialo-CBCT) images. METHODS: We developed an end-to-end automatic pipeline consisting of three main steps: (1) region of interest (ROI) computation, (2) parotid gland segmentation using the Frangi filter, and (3) ductopenia case classification with a residual neural network (RNN) augmented by multidirectional maximum intensity projection (MIP) images. To explore the impact of the first two steps, the RNN was trained on three datasets: (1) original MIP images, (2) MIP images with predefined ROIs, and (3) MIP images after segmentation. RESULTS: Evaluation was conducted on 126 parotid sialo-CBCT scans of normal, moderate, and severe ductopenic cases, yielding a high performance of 100% for the ROI computation and 89% for the gland segmentation. Improvements in accuracy and F1 score were noted among the original MIP images (accuracy: 0.73, F1 score: 0.53), ROI-predefined images (accuracy: 0.78, F1 score: 0.56), and segmented images (accuracy: 0.95, F1 score: 0.90). Notably, ductopenic detection sensitivity was 0.99 in the segmented dataset, highlighting the capabilities of the algorithm in detecting ductopenic cases. CONCLUSIONS: Our method, which combines classical image processing and deep learning techniques, offers a promising solution for automatic detection of parotid glands ductopenia in sialo-CBCT scans. This may be used for further research aimed at understanding the role of presence and severity of ductopenia in salivary gland dysfunction.
RESUMO
Cone-Beam Computed Tomography-Sialography (Sialo-CBCT) is used to demonstrate salivary ductal structure. This study aimed to conduct a volumetric analysis of the anatomical morphology of Normal-Appearing Glands (NAGs) in parotid sialo-CBCT. Our retrospective study included 14 parotid sialo-CBCT scans interpreted as NAGs in 11 patients with salivary gland impairment. The main duct length and width, as well as number and width of secondary and tertiary ducts were manually evaluated. We found that the main parotid duct showed an average width of 1.39 mm, 1.15 mm, and 0.98 mm, for the proximal, middle and distal thirds, respectively. The arborization pattern showed approximately 20% more tertiary (average number 11.1 ± 2.7) than secondary ducts (average number 9.0 ± 2.4) and approximately 8% narrower tertiary ducts (average width 0.65 ± 0.11 mm) compared to the secondary ducts (average width 0.77 ± 0.14 mm). Our anatomical analysis of NAGs in parotid sialo-CBCT demonstrated progressive narrowing of the main duct and increasing arborization and decreasing lumen size starting from the primary to the tertiary ducts. This is the most updated study regarding the anatomy of the parotid glands as demonstrated in sialo-CBCT. Our results may provide clinicians with the basic information for understanding aberration from normal morphology, as seen in salivary gland pathologies as well facilitate planning of treatment strategies, such as minimally invasive sialo-endoscopies, commonly practiced today.