A knowledge-based algorithm for automatic detection of cephalometric landmarks on CBCT images

Gupta, Abhishek; Kharbanda, Om Prakash; Sardana, Viren; Balachandran, Rajiv; Sardana, Harish Kumar

doi:10.1007/s11548-015-1173-6

Cited by 99 publications

(105 citation statements)

References 37 publications

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“…pediatrics vs. adults), missing teeth, missing parts of the region of interest, and imaging artifacts [4], [10], [11]. In 2015, Gupta et al [12] developed a knowledge-based algorithm to localize 20 anatomical landmarks on the CBCT scans. Despite the promising results, the algorithm starts with the seed detection on the anterior-inferior region of the mandible and based on a template registration.…”

Section: Related Workmentioning

confidence: 99%

Deep Geodesic Learning for Segmentation and Anatomical Landmarking

Torosdagli

Liberton

Verma

et al. 2019

IEEE Trans. Med. Imaging

107

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In this paper, we propose a novel deep learning framework for anatomy segmentation and automatic landmarking. Specifically, we focus on the challenging problem of mandible segmentation from cone-beam computed tomography (CBCT) scans and identification of 9 anatomical landmarks of the mandible on the geodesic space. The overall approach employs three inter-related steps. In step 1, we propose a deep neural network architecture with carefully designed regularization, and network hyper-parameters to perform image segmentation without the need for data augmentation and complex postprocessing refinement. In step 2, we formulate the landmark localization problem directly on the geodesic space for sparselyspaced anatomical landmarks. In step 3, we propose to use a long short-term memory (LSTM) network to identify closelyspaced landmarks, which is rather difficult to obtain using other standard detection networks. The proposed fully automated method showed superior efficacy compared to the state-of-theart mandible segmentation and landmarking approaches in craniofacial anomalies and diseased states. We used a very challenging CBCT dataset of 50 patients with a high-degree of craniomaxillofacial (CMF) variability that is realistic in clinical practice. Complementary to the quantitative analysis, the qualitative visual inspection was conducted for distinct CBCT scans from 250 patients with high anatomical variability. We have also shown feasibility of the proposed work in an independent dataset from MICCAI Head-Neck Challenge (2015) achieving the state-of-the-art performance. Lastly, we present an in-depth analysis of the proposed deep networks with respect to the choice of hyper-parameters such as pooling and activation functions.

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Section: Related Workmentioning

confidence: 99%

Deep Geodesic Learning for Segmentation and Anatomical Landmarking

Torosdagli

Liberton

Verma

et al. 2019

IEEE Trans. Med. Imaging

107

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“…Most previous automatic 3D cephalometric annotation studies employed model-based approaches which depended on a feature-extracted reference model system. (Codari et al, 2017;Gupta et al, 2015;Makram & Kamel, 2014;Montufar et al, 2018) The geometrical shape and structure of each data set is unique due to the craniofacial structural variations, making it difficult to detect the precise location of landmarks. The CNN algorithm was selected mainly because it employs a hierarchical structure to propagate information on salient features to subsequent layers while exploiting spatially local correlations.…”

Section: Discussionmentioning

confidence: 99%

Automatic three-dimensional cephalometric annotation system using three-dimensional convolutional neural networks: a developmental trial

Kang

Jeon

Kim

et al. 2019

Computer Methods in Biomechanics and Biomedical Engineering: Im

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Background: Three-dimensional (3D) cephalometric analysis using computerized tomography data has been rapidly adopted for dysmorphosis and anthropometry. Several different approaches to automatic 3D annotation have been proposed to overcome the limitations of traditional cephalometry. The purpose of this study was to evaluate the accuracy of our newly-developed system using a deep learning algorithm for automatic 3D cephalometric annotation.Methods: To overcome current technical limitations, some measures were developed to directly annotate 3D human skull data. Our deep learning-based model system mainly consisted of a 3D convolutional neural network and image data resampling.Results: The discrepancies between the referenced and predicted coordinate values in three axes and in 3D distance were calculated to evaluate system accuracy. Our new model system yielded prediction errors of 3.26, 3.18, and 4.81 mm (for three axes) and 7.61 mm (for 3D). Moreover, there was no difference among the landmarks of the three groups, including the midsagittal plane, horizontal plane, and mandible (p>0.05).Conclusion: A new 3D convolutional neural network-based automatic annotation system for 3D cephalometry was developed. The strategies used to implement the system were detailed and measurement results were evaluated for accuracy. Further development of this system is planned for full clinical application of automatic 3D cephalometric annotation.

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“…12 of the studies included in the systematic review consist of those on machine learning and super vector analysis to automatically determine cephalometric points on threedimensional or two-dimensional radiography images [4][5][6][8][9][10][11][12][13][14][15]. Artificial intelligence and super vector machine were used in 3 studies that worked on facial attractiveness and perception [16][17][18].…”

Section: Diagnosis Of Dental Deformities In Cephalometry Images Usingmentioning

confidence: 99%

“…Many orthodontists prefer not to follow cephalograms because of the time-consuming process of manual marking. Thus, an affordable, fast and automated 3-dimensional system of cephalometric marking for analysis may help diagnose the traditional disadvantages of cephalograms, such as overlapping bone structures and facial asymmetries, while increasing the effect on orthodontic practice and maintaining diagnostic protocols [1,[4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence and machine learning in orthodontics

Büyük

Hatal

2019

Ortadogu Tıp Derg

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Objective:The clinical use of information technology in orthodontics has increased significantly in recent years. The aim of this systematic review is to perform a scientific analysis of artificial intelligence and machine learning in orthodontics. Methods:An electronic search and manual search were performed on September 25, 2018 about using artificial intelligence and machine learning in orthodontics. Results:A total of 107 studies were found. Nine studies were excluded because of duplication. After exclusion of all the irrelevant and non-English articles, 23 full-text articles remained to be included in this systematic review. 3 additional articles were included in this systematic review. Twelve automatic cephalometric landmark determination, 6 orthodontic diagnosis and treatment outcomes, 2 orthodontic tooth extraction decision, 3 facial attractiveness, 1 headgear selection, 1 touchless sterilisation system and 1 automatic skeletal age determination studies were included in this systematic review. Conclusions:Artificial intelligence and machine learning are mainly focused on determination of automatic cephalometric points, facial attractiveness and tooth extraction decisions for orthodontic purposes. The use of artificial intelligence in orthodontics is important in terms of obtaining more accurate and rapid results clinically.

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A knowledge-based algorithm for automatic detection of cephalometric landmarks on CBCT images

Abstract: The proposed knowledge-based algorithm for automatic detection of landmarks on 3D images was able to achieve relatively accurate results than the currently available algorithm.

Cited by 99 publications

References 37 publications

Deep Geodesic Learning for Segmentation and Anatomical Landmarking

Deep Geodesic Learning for Segmentation and Anatomical Landmarking

Automatic three-dimensional cephalometric annotation system using three-dimensional convolutional neural networks: a developmental trial

Artificial intelligence and machine learning in orthodontics

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