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

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

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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Abbreviations

AF:

Ameloblastic fibroma

AI:

Artificial intelligence

AOT:

Adenomatoid odontogenic tumor

BL:

Bone lesions

CAm:

Cystic ameloblastoma

CBCT:

Cone beam computerized tomography

DC:

Dentigerous cyst

Faster RCNN:

Faster region-based convolutional neural networks

FOV:

Field of views

FPN:

Feature pyramid network

IAN:

Inferior alverolar nerve

KOT:

Keratocystic odontogenic tumor

LPC:

Lateral periodontal cyst

N :

Number

NC:

Nasopalatine cyst

OF:

Ossifying fibroma

RC:

Radicular cyst

ROI:

Region of interest

RPN:

Region proposal network

SBC:

Simple bone cyst

SGD:

Stochastic gradient descent

VIA:

VGG Image Annotator

w/o BL:

Without bone lesions

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Acknowledgements

We would like to acknowledge the late Prof. Isaac Leichter, who has left us unexpectedly, while still in his productive years. Prof. Leichter was a great medical physicist, researcher, and one-of-a-kind teacher and mentor. He had numerous projects throughout his lifetime, some of which are now being completed by his colleagues, in his spirit. Above all, he was kind and modest.

May he rest in peace.

Funding

This study has received only internal funding by The Jerusalem College of Technology.

Author information

Authors and Affiliations

  1. Department of Applied Physics, The Jerusalem College of Technology, Jerusalem, Israel

    Talia Yeshua, Shmuel Ladyzhensky & Isaac Leichter

  2. Oral Maxillofacial Imaging, Department of Oral Medicine, Sedation and Imaging, Faculty of Dental Medicine, Hadassah Medical Center, Hebrew University of Jerusalem, Jerusalem, Israel

    Amal Abu-Nasser

  3. Department of Oral Medicine, Sedation and Imaging, Faculty of Dental Medicine, Hadassah Medical Center, Hebrew University of Jerusalem, Jerusalem, Israel

    Ragda Abdalla-Aslan & Chen Nadler

  4. Department of Oral and Maxillofacial Surgery, Rambam Health Care Campus, Haifa, Israel

    Ragda Abdalla-Aslan

  5. Department of Software Engineering, The Jerusalem College of Technology, Jerusalem, Israel

    Tami Boharon, Avital Itzhak-Pur & Asher Alexander

  6. Department of Oral Medicine and Radiology, King George’s Medical University, Lucknow, India

    Akhilanand Chaurasia

  7. Department of Oral and Maxillofacial Surgery, Faculty of Dental Medicine, Hadassah Medical Center, Hebrew University of Jerusalem, Jerusalem, Israel

    Adir Cohen

  8. Department of Radiology, Faculty of Medicine, Hadassah Medical Center, Hebrew University of Jerusalem, Jerusalem, Israel

    Jacob Sosna & Isaac Leichter

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  1. Talia Yeshua
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Corresponding author

Correspondence to Chen Nadler.

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The scientific guarantor of this publication is Dr. Nadler Chen.

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The authors declare no competing interests.

Statistics and Biometry

One of the authors (Dr. Talia Yeshua) has significant statistical expertise.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained (HMO-0297-21).

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• This is a retrospective

• case-control observational AI study

• preformed in one institute.

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This work has been part of the DMD research study of the student, Amal Abu-Nasser, in the Hadassah Faculty of Dental Medicine, Jerusalem, Israel.

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Yeshua, T., Ladyzhensky, S., Abu-Nasser, A. et al. Deep learning for detection and 3D segmentation of maxillofacial bone lesions in cone beam CT. Eur Radiol 33, 7507–7518 (2023). https://doi.org/10.1007/s00330-023-09726-6

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  • DOI: https://doi.org/10.1007/s00330-023-09726-6

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