Recursive hierarchic segmentation analysis of bone mineral density changes on digital panoramic images

Lurie, Alan G.; Tosoni, Guilherme Monteiro; Tsimikas, John V.; Walker, Fitz

doi:10.1016/j.oooo.2011.10.002

Cited by 19 publications

(16 citation statements)

References 31 publications

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“…The segmentation of teeth, jaws and their related diseases is usually considered as a preprocessing step to complete tooth matching ( 59 – 62 ), tooth numbering ( 63 – 65 ), automatic marking of important anatomical markers, in addition to the intelligent diagnosis, classification, and prediction of diseases. Traditional methods for stomatological image segmentation include region-based ( 66 ), threshold-based ( 67 ), clustering-based ( 68 ), edge tracking ( 69 ), and watershed ( 8 ) methods. With the development of DL, many DL-based methods for stomatological image segmentation have been developed; these mainly focus on teeth, jaws, and their related diseases.…”

Section: Clinical Application Of Automatic Image Segmentation In Stomatologymentioning

confidence: 99%

Deep Learning for Automatic Image Segmentation in Stomatology and Its Clinical Application

Luo¹,

Zeng²,

Chen³

et al. 2021

Front. Med. Technol.

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Deep learning has become an active research topic in the field of medical image analysis. In particular, for the automatic segmentation of stomatological images, great advances have been made in segmentation performance. In this paper, we systematically reviewed the recent literature on segmentation methods for stomatological images based on deep learning, and their clinical applications. We categorized them into different tasks and analyze their advantages and disadvantages. The main categories that we explored were the data sources, backbone network, and task formulation. We categorized data sources into panoramic radiography, dental X-rays, cone-beam computed tomography, multi-slice spiral computed tomography, and methods based on intraoral scan images. For the backbone network, we distinguished methods based on convolutional neural networks from those based on transformers. We divided task formulations into semantic segmentation tasks and instance segmentation tasks. Toward the end of the paper, we discussed the challenges and provide several directions for further research on the automatic segmentation of stomatological images.

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Section: Clinical Application Of Automatic Image Segmentation In Stomatologymentioning

confidence: 99%

Deep Learning for Automatic Image Segmentation in Stomatology and Its Clinical Application

Luo¹,

Zeng²,

Chen³

et al. 2021

Front. Med. Technol.

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“…Many panoramic radiography-derived indices other than those discussed above have been suggested (e.g., styloid process length, alveolar bone resorption, hierarchic segmentation analysis, antegonial measurements [angles and depth], mandibular angle, and calcified carotid artery plaques), 7 11 16 28 but they have not been thoroughly evaluated in well-designed studies.…”

Section: Discussionmentioning

confidence: 99%

“… 11 Gonion index: The gonion index is the cortical thickness at the gonial angle measured on the bisector of the angle between the tangent to the posterior border of the ramus and another line tangent to the lower border of the mandible. 14 Height of the edentulous ridge: See the definition given by Kribbs et al 15 Hierarchic segmentation analysis (recursive hierarchic segmentation): See the definition given by Lurie et al 16 Incisure depth: This parameter is measured by drawing a line touching the upper limits of the condylar and coronoid processes. The longest perpendicular from this line is drawn, which is taken to be the incisure depth.…”

Section: Definitions Of the Dental Imaging-based Osteoporosis Screenimentioning

confidence: 99%

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IDIOS: An innovative index for evaluating dental imaging-based osteoporosis screening indices

et al. 2016

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PurposeThe goal of this study was to develop a new index as an objective reference for evaluating current and newly developed indices used for osteoporosis screening based on dental images. Its name; IDIOS, stands for Index of Dental-imaging Indices of Osteoporosis Screening.Materials and MethodsA comprehensive PubMed search was conducted to retrieve studies on dental imaging-based indices for osteoporosis screening. The results of the eligible studies, along with other relevant criteria, were used to develop IDIOS, which has scores ranging from 0 (0%) to 15 (100%). The indices presented in the studies we included were then evaluated using IDIOS.ResultsThe 104 studies that were included utilized 24, 4, and 9 indices derived from panoramic, periapical, and computed tomographic/cone-beam computed tomographic techniques, respectively. The IDIOS scores for these indices ranged from 0 (0%) to 11.75 (78.32%).ConclusionIDIOS is a valuable reference index that facilitates the evaluation of other dental imaging-based osteoporosis screening indices. Furthermore, IDIOS can be utilized to evaluate the accuracy of newly developed indices.

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“…The region‐based method divides an image into several regions based on discontinuities of pixel intensity. A study was carried out to explain how segmentation of dental panoramic X‐ray images assists dentists to detect osteoporosis disease [14]. Also, segmentation of bite‐wing dental X‐ray images have been possible using a region‐based approach in ref.…”

Section: Related Workmentioning

confidence: 99%

The local ternary pattern encoder–decoder neural network for dental image segmentation

Salih

Duffy

2022

IET Image Processing

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Recent advances in medical imaging analyses, especially the use of deep learning, are helping to identify, detect, classify, and quantify patterns in radiographs. At the centre of these advances is the ability to explore hierarchical feature representations learned from data. Deep learning is invaluably becoming the most sought out technique, leading to enhanced performances in the analysis of medical applications and systems. Deep learning techniques have achieved improved performance results in dental image segmentation. Segmentation of dental radiographs is a crucial step that helps dentists to diagnose dental caries. However, the performance of the deep networks used for these analyses are restrained by various challenging features found in dental carious lesions. Segmentation of dental images is often difficult due to the vast variety of types of topology, intricacies of medical structure and poor image quality caused by conditions such as low contrast, noise, irregular, and fuzzy border edges. These issues are exacerbated by low numbers of data images available for any particular analysis. A robust local ternary pattern encoder-decoder network (LTPEDN) is proposed to overcome dental image segmentation challenges and minimise the computational resources required. This new architecture is a modification of existing methods using an LTP. Images are preprocessed via augmentation and normalisation techniques to increase and prepare the datasets. Thereafter, the dataset input is sent to the LTPEDN for training and testing the model. Segmentation is performed using the nonlearnable layers (the LTP layers) and the learnable layers (standard convolution layers), to extract the ROI of the teeth. The method was evaluated on an augmented dataset of 11, 000 dental images. It was trained on 8, 800 training set images and tested on 2, 200 testing set images. The new method is shown to be 94.32% accurate.

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Recursive hierarchic segmentation analysis of bone mineral density changes on digital panoramic images

Cited by 19 publications

References 31 publications

Deep Learning for Automatic Image Segmentation in Stomatology and Its Clinical Application

Deep Learning for Automatic Image Segmentation in Stomatology and Its Clinical Application

IDIOS: An innovative index for evaluating dental imaging-based osteoporosis screening indices

The local ternary pattern encoder–decoder neural network for dental image segmentation

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