Computer-aided diagnosis of periapical cyst and keratocystic odontogenic tumor on cone beam computed tomography
Graphical abstract
Introduction
Oral diagnostic radiology comprises a sub-branch of dentistry relating to information needed for treatment planning for dental diseases. Oral examination may not always be sufficient for the diagnosis of dental disease. Therefore, during the dental examination, radiological imaging methods are utilized to assist in the examination of invisible intrabony regions. Dental images can be obtained via periapical radiography, panoramic radiography, and cone beam computed tomography (CBCT) [1].
Dental regions containing one or more tooth roots can be viewed using periapical radiographs. Panoramic radiographs are used to obtain a two-dimensional (2D) panoramic view of the upper and lower jaw. Images are acquired with the help of conventional X-rays in both periapical and panoramic devices [2].
Dental CBCT technology has been used since the late 1990s. Three-dimensional (3D) images of the neck and chin area can be obtained using this technology [3]. CBCT devices are useful in implant planning [4], teeth segmentation [5], jaw tissue segmentation [6], detection of facial traumas of the jaw [7], and obtaining 3D images of lesions and other pathologies [8].
Pathological lesions around the tooth root can be identified based on their size and location using volumetric images obtained with these devices. The data obtained by CBCT can sometimes indicate a lesion or abnormal anatomical findings. Radiology experts typically examine those images along axial, sagittal, and coronal planes and can thereby readily make determinations about the findings [9], [10].
Detected abnormalities are often expressed as endodontic or periapical lesions [11], [12]. During radiological examinations, it is important for the doctor to understand the characteristics of detected lesions in a timely and appropriate manner. According to current studies, volumetric measurements on a CBCT scan are an accurate indicator of the volume of the actual periapical lesion in the jawbone [13], [14]. As a result, the planned treatment can be effective in reversing the destruction of the pathology, which supports the importance of CBCT and other radiological examination methods [2].
Lesions detected as a result of scanning are evaluated with consideration of some of their properties, such as location, periphery, and internal structures in the surrounding area [3]. Some lesions have the potential to develop into cancer. Early diagnosis of such lesions is crucial to preventing disease progression [1].
In recent years, many studies have been performed in the areas of medical imaging and signal processing. A majority of these studies address the classification of medical information. In each study, a specific method is preferred depending on the characteristic of the disease of interest. Classification methods are used to obtain meaningful results from medical information. Feature extraction processes are applied to that medical information for classification experiments. In accordance with the content of the medical information, various features are used in each study. The power spectral density technique, for example, may be useful for extracting features from the obtained signal data in research conducted on a brain computer interface [15]. To establish a good classifier in a study performed on a retinal image database, features such as shape, texture, and intensity may be suitable for determining the image quality [16].
Lesion classification is a sub-problem in the field of computer vision. Dental structures located in the head and neck area do not have a specific form. Therefore, research conducted on dental images in the field of computer vision is extremely challenging. In the present study, we employ textural features to address the formal diversity observed in dental images. Unlike research in medical image processing in other disciplines, few published studies exist that address computer-aided detection and classification of apical lesions [17], [18], [19], [20], [21].
In this article, we discuss classification of periapical cyst and keratocystic odontogenic tumor (KCOT) lesions observed in 50 different dental CBCT datasets. These datasets were identified by experts as periapical cysts and KCOT lesions according to the clinical, radiographic, and histopathologic features. KCOT was previously referred to as the odontogenic keratocyst (OKC). It was reclassified as the keratocystic odontogenic tumor by the World Health Organization (WHO) in 2005 [22], [23]. The KCOT lesion is known for its aggressive behavior and high recurrence rate [24], [25]. It is therefore necessary to especially focus on differentiating KCOT from cystic lesions. The aim of this study was thus to develop a CBCT classifier method for exact differentiation of KCOT from other cystic lesions.
Section snippets
Materials and methods
Unlike previous research, we used a new 3D dental image dataset. Accordingly, we further extracted a new feature vector derived from this dataset. In our experiments, lesions were classified into two groups using ten-fold cross validation, split sample validation and leave-one-out cross validation (LOOCV) methods. Fig. 1 presents a flow diagram of the operations performed to identify the most effective model for correctly distinguishing periapical cyst and KCOT lesion [22] images obtained via
Results
In the first experiment group, the full 50 feature vector with 636 attributes was used for the model application. Ten-fold cross validation was selected for model validation in the training and testing phases. As a result of the multiple experiments conducted, the SVM classifier achieved the best performance of 94.00% accuracy, 94.00% F1 with the ability to cope with many independent variables. That result was obtained by SVM with an analysis of variance (ANOVA) kernel structure, which performs
Discussion
The main objective of this study was to classify periapical cyst and KCOT lesions detected on volumetric images obtained by CBCT. A few studies have been conducted in the field of computer-aided diagnosis of dental apical lesions. Some of these studies were performed on images obtained with panoramic imaging methods. Published studies using CBCT are in early stages.
One of these studies involved classification of jaw bone cysts and necrosis via the processing of panoramic radiographs [19]. In
Conclusion
Endodontic lesions are amorphous in shape; thus, results of the segmentation implemented by different oral radiologist may vary. Despite this limitation, a bounding box that surrounds the lesion in 3D space was considered for feature extraction of the segmented lesion in this study. We are currently developing a hybrid segmentation method that will provide more accurate segmentation results.
In contrast to previous studies, we conducted our experiments with a larger dataset consisting of images
Conflict of interest
No conflicts of interest are declared.
Acknowledgements
The authors would like to thank Dr. Umit Cobanoglu, Dr. Celal Candirli, Dr. Nuray Yilmaz Altintas, Dr. Yavuz Tolga Korkmaz, Oral Diagnosis and Radiology Department of Karadeniz Technical University for providing the dataset used in this study. The data referenced in this study were obtained and used with the permission of Karadeniz Technical University Clinical Research Ethics Committee.
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