Deep learning for detection and 3D segmentation of maxillofacial bone lesions in cone beam CT

• Published in: European radiology Vol. 33; no. 11; pp. 7507 - 7518
• Main Authors: Yeshua, Talia, Ladyzhensky, Shmuel, Abu-Nasser, Amal, Abdalla-Aslan, Ragda, Boharon, Tami, Itzhak-Pur, Avital, Alexander, Asher, Chaurasia, Akhilanand, Cohen, Adir, Sosna, Jacob, Leichter, Isaac, Nadler, Chen
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2023; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Axial skeleton; Bone imaging; Bone lesions; Computed tomography; Cone-Beam Computed Tomography - methods; Datasets; Deep Learning; Diagnostic Radiology; Humans; Image Processing, Computer-Assisted; Image segmentation; Imaging; Imaging Informatics and Artificial Intelligence; Internal Medicine; Interventional Radiology; Jaw; Lesions; Machine learning; Mathematical analysis; Maxillofacial; Medical imaging; Medicine; Medicine & Public Health; Morbidity; Neuroradiology; Radiology; Segmentation; Ultrasound; Deep learning; Pathology; Cone beam computed tomography; Oral; Incidental findings
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-023-09726-6

Objectives To develop an automated deep-learning algorithm for detection and 3D segmentation of incidental bone lesions in maxillofacial CBCT scans. Methods The dataset included 82 cone beam CT (CBCT) scans, 41 with histologically confirmed benign bone lesions (BL) and 41 control scans (without lesions), obtained using 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 (4530 axial images), and testing (6795 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 3D segmentations 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 axial images 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. Clinical relevance Our novel deep-learning algorithm detects incidental hypodense bone lesions in cone beam CT scans, using various imaging devices and protocols. This algorithm may reduce patients’ morbidity and mortality, particularly since currently, cone beam CT interpretation is not always preformed. Key Points • A deep learning algorithm was developed for automatic detection and 3D segmentation of various maxillofacial bone lesions in CBCT scans, irrespective of the CBCT device or the scanning protocol. • The developed algorithm can detect incidental jaw lesions with high accuracy, generates a 3D segmentation of the lesion, and calculates the lesion volume.