Contemporary diagnostic imaging decisions, based on the results of imaging technologies, have become a significant part of medical decision-making. There is a wide variety of pathological lesions that affect the dentomaxillofacial area, with radiolucent, radio-opaque, and mixed radiolucent-radio-opaque appearances. Benign and malignant lesions with odontogenic and nonodontogenic origins and have variable degrees of damaging potential.

The diagnosis of these lesions is challenging because many of them frequently exhibit similar imaging appearances, resulting in uncertainty in diagnosis. New devices and software tools can assist the radiologist with classification, localisation, and orientation of these lesions. Different scanning techniques, such as CT, CBCT, and MRI, and new protocols have a high capability for gathering 3D quantitative information on target tissues, as well as identifying biomarkers for prognosis and treatment response for pathological lesions. Computer-aided diagnosis systems report the same steps, such as pre-processing, segmentation, parameter extraction, and classification of lesions.

This aim of this Special Issue is to invite papers showing a variety of different points of view presented by diverse authors covering diverse topics related to advances in imaging techniques for dentomaxillofacial lesions. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Classification of jaw lesions based on segmentation of images
• Clustering techniques for jaw lesion analysis
• Deep learning computer-aided diagnosis
• Detection of maxillofacial lesions with filtering techniques
• Image fusion using CT, MRI, and PET
• Image segmentation and extraction using CBCT, CT, and MRI
• Imaging classification of pathological jaw lesions
• Medical image processing software
• Panoramic X-ray, CBCT, CT, and MRI analysis of pathological jaw lesions
• Texture analysis for classification of maxillofacial lesions
• Three-dimensional lesion classification in CT images