Cascaded convolutional networks for automatic cephalometric landmark detection

Zeng, Minmin; Yan, Zhenlei; Liu, Shuai; Zhou, Yanheng; Qiu, Lixin

doi:10.1016/j.media.2020.101904

Cited by 68 publications

(49 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the case of private cephalograms, the model showed a slight reduction in the SDR within a 2 mm threshold. This result was superior or similar to those from previous studies [3,4,12]. In a previous study, an even more dramatic drop in the accuracy was observed when the models were tested on a fully external dataset [3,4].…”

Section: Performance Of the Dacfl Model On The Private Datasetsupporting

confidence: 82%

“…Overall, the results for the private dataset were inferior to those for the public dataset with standardized images [2][3][4][5][6]9,15]. [2][3][4][5][6]9,15], possibly showing high comparability, but limited generalizability. Therefore, testing broad data can demonstrate the generalizability and robustness of the model.…”

Section: Performance Of the Dacfl Model On The Private Datasetmentioning

confidence: 78%

See 1 more Smart Citation

Effectiveness of Human–Artificial Intelligence Collaboration in Cephalometric Landmark Detection

Kang

et al. 2022

JPM

View full text Add to dashboard Cite

Detection of cephalometric landmarks has contributed to the analysis of malocclusion during orthodontic diagnosis. Many recent studies involving deep learning have focused on head-to-head comparisons of accuracy in landmark identification between artificial intelligence (AI) and humans. However, a human–AI collaboration for the identification of cephalometric landmarks has not been evaluated. We selected 1193 cephalograms and used them to train the deep anatomical context feature learning (DACFL) model. The number of target landmarks was 41. To evaluate the effect of human–AI collaboration on landmark detection, 10 images were extracted randomly from 100 test images. The experiment included 20 dental students as beginners in landmark localization. The outcomes were determined by measuring the mean radial error (MRE), successful detection rate (SDR), and successful classification rate (SCR). On the dataset, the DACFL model exhibited an average MRE of 1.87 ± 2.04 mm and an average SDR of 73.17% within a 2 mm threshold. Compared with the beginner group, beginner–AI collaboration improved the SDR by 5.33% within a 2 mm threshold and also improved the SCR by 8.38%. Thus, the beginner–AI collaboration was effective in the detection of cephalometric landmarks. Further studies should be performed to demonstrate the benefits of an orthodontist–AI collaboration.

show abstract

Section: Performance Of the Dacfl Model On The Private Datasetsupporting

confidence: 82%

Section: Performance Of the Dacfl Model On The Private Datasetmentioning

confidence: 78%

Effectiveness of Human–Artificial Intelligence Collaboration in Cephalometric Landmark Detection

Kang

et al. 2022

JPM

View full text Add to dashboard Cite

show abstract

“…Another advantage of our probabilistic approach is that we can provide the confidence regions of predictions. This property is important in treatment planning since clinicians need to review and correct estimated landmark positions [32,192] when τ is 2.0 mm, 2.5 mm, 3.0 mm and 4.0 mm.…”

Section: Comparison With Existing Methodsmentioning

confidence: 99%

Multistage Probabilistic Approach for the Localization of Cephalometric Landmarks

et al. 2021

View full text Add to dashboard Cite

The accurate and reproducible localization of cephalometric landmarks is an important procedure for treatment planning and clinical practice in orthodontics and maxillofacial surgery. In this paper, we propose a new multistage cephalometric landmark localization method that exploits local appearances and global characteristics simultaneously. To be precise, a convolutional neural network(CNN) is trained by minimizing the sum of all landmark errors. Since landmarks are considered simultaneously, global hard/soft tissue characteristics, as well as landmark relations, can be reflected in this stage. Then, we exploit local appearances by using high-resolution cropped images. In this second stage, we train CNNs for individual landmarks, respectively. Finally, we improve the localization performance of cephalometric landmarks of the mandible with linear estimators. Experiments on ISBI2015 dataset have shown that the proposed method outperforms conventional methods. Also, the proposed method allows us to evaluate confidence (e.g., standard deviational ellipses) due to its probabilistic formulation. INDEX TERMS Cephalometric landmark detection, cephalometry, dental radiography.

show abstract

“…An approach with a cascaded three-stage convolutional neural networks (Zeng et al, 2021) predicts cephalometric landmarks automatically in 2D radiograms. Initially, the lateral face area is located using high-level features of the craniofacial structures.…”

Section: Related Workmentioning

confidence: 99%

Automatic Anthropological Landmarks Recognition and Measurements

Knyaz

Maksimov

Novikov

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. Many anthropological researches require identification and measurement of craniometric and cephalometric landmarks which provide valuable information about the shape of a head. This information is necessary for morphometric analysis, face approximation, craniafacial identification etc. Traditional techniques use special anthropological tools to perform required measurements, identification of landmarks usually being made by an expert-anthropologist. Modern techniques of optical 3D measurements such as photogrammetry, computer tomography, laser 3D scanning provide new possibilities for acquiring accurate 2D and 3D data of high resolution, thus creating new conditions for anthropological data analysis. Traditional anthropological manual point measurements can be substituted by analysis of accurate textured 3D models, which allow to retrieve more information about studied object and easily to share data for independent analysis. The paper presents the deep learning technique for anthropological landmarks identification and accurate 3D measurements. Photogrammetric methods and their practical implementation in the automatic system for accurate digital 3D reconstruction of anthropological objects are described.

show abstract

Cascaded convolutional networks for automatic cephalometric landmark detection

Cited by 68 publications

References 23 publications

Effectiveness of Human–Artificial Intelligence Collaboration in Cephalometric Landmark Detection

Effectiveness of Human–Artificial Intelligence Collaboration in Cephalometric Landmark Detection

Multistage Probabilistic Approach for the Localization of Cephalometric Landmarks

Automatic Anthropological Landmarks Recognition and Measurements

Contact Info

Product

Resources

About