Evaluation of the reliability of computerized profile cephalometric analysis

Ferreira, José Tarcísio Lima; Telles, Carlos de Souza

doi:10.1590/s0103-64402002000300012

Cited by 25 publications

(16 citation statements)

References 12 publications

(15 reference statements)

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“…14,16 With regard to angular measurement SNGn, although it is a measurement that involves the landmark N, which is difficult to locate in both methods, it is not statistically different in both methods of cephalometric tracing. 8,18 The angular measurement IMPA is easily measurable, as it does not involve hard-to-find landmarks described in this study. It also did not show statistically significant changes in both methods.…”

Section: Discussionmentioning

confidence: 99%

“…3,4,6,7,12,16 Justifications can be made with regard to the selection of landmarks and cephalometric measurements in this type of work, as exemplified in the ease of locating the landmarks, providing higher reliability and precision, which can directly influence the measurement, 13,17,18 as the reproducibility of the measurement is part of different types of cephalometric analysis proposed by several authors. 2,5,7,8,11,16 The manual method required higher time expenditure, but it is the most common method for tracing, identifying landmarks, measuring distances and angles between the locations of the landmarks, 15 in addition to having a high possibility of error. The authors recommended the replication of tracings as a good measure to diminish the possibility of error with this method.…”

Section: Discussionmentioning

confidence: 99%

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Estudo comparativo das análises cefalométricas manual e computadorizada

Guedes

Souza

Tuji

et al. 2010

Dental Press J. Orthod.

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OBJETIVO: realizar uma análise comparativa dos traçados manual e computadorizado utilizando um software específico, com a finalidade de definir os resultados inter e intra-avaliadores. MÉTODOS: foi utilizada uma amostra composta por 50 radiografias cefalométricas em norma lateral, sendo todas padronizadas, contendo pacientes de ambos os gêneros e de várias faixas etárias. A análise das radiografias foi realizada por dois avaliadores, os quais realizaram os traçados manuais e computadorizados das 50 radiografias. Para compor as medições, foram selecionadas medidas angulares e lineares, que posteriormente foram submetidas ao teste estatístico de Mann-Whitney, com o objetivo de comparar os resultados entre os dois tipos de traçados inter e intra-avaliadores. RESULTADOS E CONCLUSÕES: conclui-se que pode ser aumentada a confiança nos traçados cefalométricos computadorizados, haja vista que as discrepâncias encontradas entre as medidas dos traçados cefalométricos manual e computadorizado inter e intra-avaliadores, em sua maioria, não foram estatisticamente significativas.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Estudo comparativo das análises cefalométricas manual e computadorizada

Guedes

Souza

Tuji

et al. 2010

Dental Press J. Orthod.

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“…Cephalometric analysis is performed on skeletal X-ray images. This is necessary for doctors to make orthodontic diagnoses [1][2][3]. In cephalometric analysis, the first step is to detect landmarks in X-ray images.…”

Section: Introductionmentioning

confidence: 99%

Automatic Cephalometric Landmark Detection on X-ray Images Using a Deep-Learning Method

Shen

Iwamoto

et al. 2020

Applied Sciences

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Accurate automatic quantitative cephalometry are essential for orthodontics. However, manual labeling of cephalometric landmarks is tedious and subjective, which also must be performed by professional doctors. In recent years, deep learning has gained attention for its success in computer vision field. It has achieved large progress in resolving problems like image classification or image segmentation. In this paper, we propose a two-step method which can automatically detect cephalometric landmarks on skeletal X-ray images. First, we roughly extract a region of interest (ROI) patch for each landmark by registering the testing image to training images, which have annotated landmarks. Then, we utilize pre-trained networks with a backbone of ResNet50, which is a state-of-the-art convolutional neural network, to detect each landmark in each ROI patch. The network directly outputs the coordinates of the landmarks. We evaluate our method on two datasets: ISBI 2015 Grand Challenge in Dental X-ray Image Analysis and our own dataset provided by Shandong University. The experiments demonstrate that the proposed method can achieve satisfying results on both SDR (Successful Detection Rate) and SCR (Successful Classification Rate). However, the computational time issue remains to be improved in the future. a global-context shape model. In 2017, Ibragimov et al. added a convolutional neural network for binary classification to their conventional method. They surpass the result of Lindner's a little bit, with around 75.3% prediction accuracy within a 2-mm range [9]. In 2017, Hansang Lee et al. proposed a deep learning based method which achieved not bad results but in a small resized image. He trained two networks to regress the landmark's x and y coordinate directly [10]. In 2019, Jiahong Qian et al. proposed a new architecture called CephaNet which improves the architecture of Faster R-CNN [11,12].Its accuracy is nearly 6% higher than other conventional methods.Despite the variety of techniques available, automatic cephalometric landmark detection remains insufficient due to its limited accuracy. In recent years, deep learning has gained attention for its success in computer vision field. For example, convolutional neural network models are widely used in problems like landmark detection [13,14], image classification [15][16][17] and image segmentation [18,19]. Trained models' performances surpass that of human beings in many applications. Since direct regression of several landmarks is a highly non-linear mapping, which is difficult to learn [20][21][22][23]. In our method, we only try to detect one key point in one patch image. We learn a non-linear mapping function for only one key point. Each key point has its corresponding non-linear mapping function. So we can achieve more accurate detection of key point than other methods.In this paper, we propose a two-step method for the automatic detection of cephalometric landmarks. First, we get the coarse landmark location by registering the test image to a most similar image in...

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“…[1][2][3][4][5][6][7] Such analyses are usually performed using two-dimensional (2-D) x-ray images of the human head, i.e., cephalograms, because of the high resolution enabled by modern x-ray imaging devices and the superior distinguishability of the bony structure boundaries in x-ray images. A standard cephalometric analysis procedure involves identification of anatomically relevant anchor points, i.e., landmarks, measurement of various angles and distances between these landmarks, and qualitative assessment of pathologies from these angles and distances.…”

Section: Introductionmentioning

confidence: 99%

Fully automated quantitative cephalometry using convolutional neural networks

Arık¹,

2017

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Abstract. Quantitative cephalometry plays an essential role in clinical diagnosis, treatment, and surgery. Development of fully automated techniques for these procedures is important to enable consistently accurate computerized analyses. We study the application of deep convolutional neural networks (CNNs) for fully automated quantitative cephalometry for the first time. The proposed framework utilizes CNNs for detection of landmarks that describe the anatomy of the depicted patient and yield quantitative estimation of pathologies in the jaws and skull base regions. We use a publicly available cephalometric x-ray image dataset to train CNNs for recognition of landmark appearance patterns. CNNs are trained to output probabilistic estimations of different landmark locations, which are combined using a shape-based model. We evaluate the overall framework on the test set and compare with other proposed techniques. We use the estimated landmark locations to assess anatomically relevant measurements and classify them into different anatomical types. Overall, our results demonstrate high anatomical landmark detection accuracy (∼1% to 2% higher success detection rate for a 2-mm range compared with the top benchmarks in the literature) and high anatomical type classification accuracy (∼76% average classification accuracy for test set). We demonstrate that CNNs, which merely input raw image patches, are promising for accurate quantitative cephalometry.

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Evaluation of the reliability of computerized profile cephalometric analysis

Cited by 25 publications

References 12 publications

Estudo comparativo das análises cefalométricas manual e computadorizada

Estudo comparativo das análises cefalométricas manual e computadorizada

Automatic Cephalometric Landmark Detection on X-ray Images Using a Deep-Learning Method

Fully automated quantitative cephalometry using convolutional neural networks

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