Automatic Landmark Identification in Lateral Cephalometric Images Using Optimized Template Matching

Vasamsetti, Srikanth; Sardana, Viren; Kumar, P.Y.; Kharbanda, Om Prakash; Sardana, Harish Kumar

doi:10.1166/jmihi.2015.1426

Cited by 12 publications

(3 citation statements)

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“…Cephalometric analysis of teleradiography of the lateral projection of the head in the lateral projection is a labor-intensive diagnostic method that requires the doctor's outstanding experience and high qualification. In the process of cephalometric analysis, the complexity of deciphering a picture arises due to the individual structural features of the facial skeleton [5], the overlapping of anatomical structures, the presence of tooth rudiments. Improving the technologies of methods of radiology diagnostics and interpretation of the results obtained using such methods requires the introduction of modern information technologies into the practice of an orthodontist.…”

Section: Introductionmentioning

confidence: 99%

The efficiency of deep learning algorithms for detecting anatomical reference points on radiological images of the head profile

Dobratulin

Gaidel

Kapishnikov

et al. 2020

2020 International Conference on Information Technology and Nanotechnology (ITNT)

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In this article we investigate the efficiency of deep learning algorithms in solving the task of detecting anatomical reference points on radiological images of the head in lateral projection using a fully convolutional neural network and a fully convolutional neural network with an extended architecture for biomedical image segmentation -U-Net. A comparison is made for the results of detection anatomical reference points for each of the selected neural network architectures and their comparison with the results obtained when orthodontists detected anatomical reference points. Based on the obtained results, it was concluded that a U-Net neural network allows performing the detection of anatomical reference points more accurately than a fully convolutional neural network. The results of the detection of anatomical reference points by the U-Net neural network are closer to the average results of the detection of reference points by a group of orthodontists.

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Section: Introductionmentioning

confidence: 99%

The efficiency of deep learning algorithms for detecting anatomical reference points on radiological images of the head profile

Dobratulin

Gaidel

Kapishnikov

et al. 2020

2020 International Conference on Information Technology and Nanotechnology (ITNT)

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“…The social significance of the diagnosis and treatment of dentoalveolar anomalies is due to their high prevalence, the tendency to an increase in the number of patients with this pathology, disorders from other organs and body systems associated with these pathologies [1]. The main research method in orthodontics for the diagnosis of anomalies and treatment planning is the cephalometric analysis of teleradiographic images in the lateral projection [2]. This analysis is a laborious and time-consuming research method that requires the proper experience and qualifications of a doctor [3].…”

Section: Introductionmentioning

confidence: 99%

Development of a software complex for the diagnosis of dentoalveolar anomalies using neural networks

Колсанов¹,

Попов²,

Aiupova³

et al. 2021

Preprint

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This article describes the goals and objectives of developing a software complex for planning the treatment of dentoalveolar anomalies, the architecture of the software complex as interacting components for treatment planning, as well as the principle of using algorithms using convolutional neural networks within the software complex for a component that solves the problem of decoding a teleradiographic image.

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“…It can be observed that there is a plethora of scientific literature on algorithms for 2D cephalometric landmark identification on X-ray radiographs. 1,2 There are several limitations in conventional 2D radiographs such as nonlinear magnification, distortion, projected measurements and overlapping of craniofacial structures 3,4 etc. Though, with technological progress and evolution of cone-beam computed tomography (CBCT), limitations associated with 2D conventional radiographs have been overcome.…”

Section: Introductionmentioning

confidence: 99%

Automatic localization of three-dimensional cephalometric landmarks on CBCT images by extracting symmetry features of the skull

Neelapu

Kharbanda

Sardana

et al. 2018

Dentomaxillofacial Radiology

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To propose an algorithm for automatic localization of 3D cephalometric landmarks on CBCT data, those are useful for both cephalometric and upper airway volumetric analysis. 20 landmarks were targeted for automatic detection, of which 12 landmarks exist on the mid-sagittal plane. Automatic detection of mid-sagittal plane from the volume is a challenging task. Mid-sagittal plane is detected by extraction of statistical parameters of the symmetrical features of the skull. The mid-sagittal plane is partitioned into four quadrants based on the boundary definitions extracted from the human anatomy. Template matching algorithm is applied on the mid-sagittal plane to identify the region of interest ROI, further the edge features are extracted, to form contours in the individual regions. The landmarks are automatically localized by using the extracted knowledge of anatomical definitions of the landmarks. The overall mean error for detection of 20 landmarks was 1.88 mm with a standard deviation of 1.10 mm. The cephalometric land marks on CBCT data were detected automatically with in the mean error less than 2 mm.

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Automatic Landmark Identification in Lateral Cephalometric Images Using Optimized Template Matching

Cited by 12 publications

References 0 publications

The efficiency of deep learning algorithms for detecting anatomical reference points on radiological images of the head profile

The efficiency of deep learning algorithms for detecting anatomical reference points on radiological images of the head profile

Development of a software complex for the diagnosis of dentoalveolar anomalies using neural networks

Automatic localization of three-dimensional cephalometric landmarks on CBCT images by extracting symmetry features of the skull

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