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Yeshua T, Ladyzhensky S, Abu-Nasser A, Abdalla-Aslan R, Boharon T, Itzhak-Pur A, Alexander A, Chaurasia A, Cohen A, Sosna J, Leichter I, Nadler C. Deep learning for detection and 3D segmentation of maxillofacial bone lesions in cone beam CT. Eur Radiol 2023; 33:7507-7518. [PMID: 37191921 DOI: 10.1007/s00330-023-09726-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/30/2023] [Accepted: 04/21/2023] [Indexed: 05/17/2023]
Abstract
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.
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Affiliation(s)
- Talia Yeshua
- Department of Applied Physics, The Jerusalem College of Technology, Jerusalem, Israel
| | - Shmuel Ladyzhensky
- Department of Applied Physics, The Jerusalem College of Technology, Jerusalem, Israel
| | - Amal Abu-Nasser
- Oral Maxillofacial Imaging, Department of Oral Medicine, Sedation and Imaging, Faculty of Dental Medicine, Hadassah Medical Center, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ragda Abdalla-Aslan
- Department of Oral Medicine, Sedation and Imaging, Faculty of Dental Medicine, Hadassah Medical Center, Hebrew University of Jerusalem, Jerusalem, Israel
- Department of Oral and Maxillofacial Surgery, Rambam Health Care Campus, Haifa, Israel
| | - Tami Boharon
- Department of Software Engineering, The Jerusalem College of Technology, Jerusalem, Israel
| | - Avital Itzhak-Pur
- Department of Software Engineering, The Jerusalem College of Technology, Jerusalem, Israel
| | - Asher Alexander
- Department of Software Engineering, The Jerusalem College of Technology, Jerusalem, Israel
| | - Akhilanand Chaurasia
- Department of Oral Medicine and Radiology, King George's Medical University, Lucknow, India
| | - Adir Cohen
- Department of Oral and Maxillofacial Surgery, Faculty of Dental Medicine, Hadassah Medical Center, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Jacob Sosna
- Department of Radiology, Faculty of Medicine, Hadassah Medical Center, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Isaac Leichter
- Department of Applied Physics, The Jerusalem College of Technology, Jerusalem, Israel
- Department of Radiology, Faculty of Medicine, Hadassah Medical Center, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Chen Nadler
- Department of Oral Medicine, Sedation and Imaging, Faculty of Dental Medicine, Hadassah Medical Center, Hebrew University of Jerusalem, Jerusalem, Israel.
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