Tooth detection and numbering in panoramic radiographs using convolutional neural networks

Autor: Michael M. Bornstein, Sergey I. Nikolenko, Lyudmila N. Tuzova, Max A. Kharchenko, Dmitry V. Tuzoff, Alexey S. Krasnov, Georgiy B. Bednenko, Mikhail M. Sveshnikov
Rok vydání: 2019
Předmět:
Adult
Panoramic radiograph
Computer science
Radiography
DIAGNOSIS
Convolutional neural network
CLASSIFICATION
030218 nuclear medicine & medical imaging
Interpretation (model theory)
TEETH
03 medical and health sciences
0302 clinical medicine
stomatognathic system
Radiography
Panoramic
Dentistry
Oral Surgery & Medicine
convolutional neural networks
Humans
Radiology
Nuclear Medicine and imaging
Computer vision
Diagnosis
Computer-Assisted
radiographic image interpretation
General Dentistry
Science & Technology
Computer aided diagnostics
business.industry
Radiology
Nuclear Medicine & Medical Imaging
Process (computing)
030206 dentistry
General Medicine
computer-aided diagnostics
Numbering
respiratory tract diseases
Clinical Practice
stomatognathic diseases
Otorhinolaryngology
teeth detection and numbering
panoramic radiograph
Neural Networks
Computer
Artificial intelligence
business
Tooth
Life Sciences & Biomedicine
Algorithms
Research Article
Zdroj: Dentomaxillofac Radiol
ISSN: 1476-542X
0250-832X
DOI: 10.1259/dmfr.20180051
Popis: OBJECTIVES: Analysis of dental radiographs is an important part of the diagnostic process in daily clinical practice. Interpretation by an expert includes teeth detection and numbering. In this project, a novel solution based on convolutional neural networks (CNNs) is proposed that performs this task automatically for panoramic radiographs. METHODS: A data set of 1352 randomly chosen panoramic radiographs of adults was used to train the system. The CNN-based architectures for both teeth detection and numbering tasks were analyzed. The teeth detection module processes the radiograph to define the boundaries of each tooth. It is based on the state-of-the-art Faster R-CNN architecture. The teeth numbering module classifies detected teeth images according to the FDI notation. It utilizes the classical VGG-16 CNN together with the heuristic algorithm to improve results according to the rules for spatial arrangement of teeth. A separate testing set of 222 images was used to evaluate the performance of the system and to compare it to the expert level. RESULTS: For the teeth detection task, the system achieves the following performance metrics: a sensitivity of 0.9941 and a precision of 0.9945. For teeth numbering, its sensitivity is 0.9800 and specificity is 0.9994. Experts detect teeth with a sensitivity of 0.9980 and a precision of 0.9998. Their sensitivity for tooth numbering is 0.9893 and specificity is 0.9997. The detailed error analysis showed that the developed software system makes errors caused by similar factors as those for experts. CONCLUSIONS: The performance of the proposed computer-aided diagnosis solution is comparable to the level of experts. Based on these findings, the method has the potential for practical application and further evaluation for automated dental radiograph analysis. Computer-aided teeth detection and numbering simplifies the process of filling out digital dental charts. Automation could help to save time and improve the completeness of electronic dental records. ispartof: DENTOMAXILLOFACIAL RADIOLOGY vol:48 issue:4 ispartof: location:England status: published
Databáze: OpenAIRE
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