Objectives: To develop an automated deep-learning algorithm for detection and 3D segmentation of incidental bone lesions in maxillofacial CBCT scans.Methods: Dataset included 82 cone beam CT (CBCT) scans, 41 with histologically confirmed benign bone lesions and 41 control scans (with no lesion), obtained by three CBCT devices with diverse imaging protocols. Lesions were marked in all axial slices by experienced maxillofacial radiologists. All cases were divided into sub-datasets: training (20,214 axial images), validation (4,530 axial images) and testing (6,795 axial images). A Mask-RCNN algorithm segmented the bone lesions in each axial slice. Analysis of sequential slices was used for improving the Mask-RCNN performance and classifying each CBCT scan as containing bone lesions or not. Finally, the algorithm generated a 3D segmentation of the lesions and calculated their volumes.Results: The algorithm correctly classified all CBCT cases as containing bone lesions or not, with an accuracy of 100%. The algorithm detected the bone lesion in each axial image with high sensitivity (95.9%) and high precision (98.9%) with an average dice coefficient of 83.5%. Conclusions:The developed algorithm detected and segmented bone lesions in CBCT scans with high accuracy and may serve as a computerized tool for detecting incidental bone lesions in CBCT imaging.

Objectives: This study aimed to develop an automated deep-learning algorithm for the detection and 3D segmentation of incidental jaw lesions in maxillofacial CBCT scans.Materials and Methods: The dataset included 82 CBCT scans with and without histologically confirmed benign bone lesions, obtained from three CBCT devices using different imaging protocols. The dataset consisted of axial CBCT images and was divided into training dataset (20,214 axial images), validation dataset (4,530 axial images) and testing dataset (6,795 axial images). A Mask-RCNN based deep-learning algorithm segmented the bone lesion in each axial image. The analysis of sequential slices improved the mask RCNN performance and assisted in classifying each CBCT case as containing bone lesions or not. Thereafter, the algorithm generated a 3D segmentation of the lesions. Results: The accuracy of the algorithm for classifying each CBCT case as either containing bone lesions or not, was 100%. The algorithm performance for detecting lesions in individual axial images showed high sensitivity (95.9%) and high precision (98.9%).Conclusions: Our deep learning algorithm can detect incidental bone lesions in CBCT scans with high accuracy, high sensitivity and high precision and is highly recommended for bone lesion detection and follow-up in CBCT imaging. Clinical relevance: The increasing number of CBCT scans performed worldwide dictates a clinical need for developing an automated tool, which will review, detect and volumetrically demonstrate incidental bone lesions in CBCT scans with high accuracy and low false-positive rate.

Objectives: This study aimed to develop an automated deep-learning algorithm for the detection and 3D segmentation of incidental jaw lesions in maxillofacial CBCT scans.Materials and Methods: The dataset included 82 CBCT scans with and without histologically confirmed benign bone lesions, obtained from three CBCT devices using different imaging protocols. The dataset consisted of axial CBCT images and was divided into training dataset (20,214 axial images), validation dataset (4,530 axial images) and testing dataset (6,795 axial images). A Mask-RCNN based deep-learning algorithm segmented the bone lesion in each axial image. The analysis of sequential slices improved the mask RCNN performance and assisted in classifying each CBCT case as containing bone lesions or not. Thereafter, the algorithm generated a 3D segmentation of the lesions. Results: The accuracy of the algorithm for classifying each CBCT case as either containing bone lesions or not, was 100%. The algorithm performance for detecting lesions in individual axial images showed high sensitivity (95.9%) and high precision (98.9%).Conclusions: Our deep learning algorithm can detect incidental bone lesions in CBCT scans with high accuracy, high sensitivity and high precision and is highly recommended for bone lesion detection and follow-up in CBCT imaging. Clinical relevance: The increasing number of CBCT scans performed worldwide dictates a clinical need for developing an automated tool, which will review, detect and volumetrically demonstrate incidental bone lesions in CBCT scans with high accuracy and low false-positive rate.