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

Kang, Sung Ho; Jeon, Kiwan; Kim, Hak Jin; Seo, Jin Keun; Lee, Sang Hwy

doi:10.1080/21681163.2019.1674696

Cited by 22 publications

(17 citation statements)

References 46 publications

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“…The result suggests a potential role of the initial two-dimensional search algorithm in the improvement of accuracy and time saving for three-dimensional landmark annotation. Deep learning methods like CNN structure have also been conducted ( Kang et al, 2020 ; Lee et al, 2019 ; Yun et al, 2020 ). Some structures like gonion, porion and others seem to be points with imperfect accuracy.…”

Section: Applications Of ML In the Dental Oral And Craniofacial Imaging Fieldmentioning

confidence: 99%

Machine learning in dental, oral and craniofacial imaging: a review of recent progress

Ren

Luo

et al. 2021

PeerJ

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Artificial intelligence has been emerging as an increasingly important aspect of our daily lives and is widely applied in medical science. One major application of artificial intelligence in medical science is medical imaging. As a major component of artificial intelligence, many machine learning models are applied in medical diagnosis and treatment with the advancement of technology and medical imaging facilities. The popularity of convolutional neural network in dental, oral and craniofacial imaging is heightening, as it has been continually applied to a broader spectrum of scientific studies. Our manuscript reviews the fundamental principles and rationales behind machine learning, and summarizes its research progress and its recent applications specifically in dental, oral and craniofacial imaging. It also reviews the problems that remain to be resolved and evaluates the prospect of the future development of this field of scientific study.

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Section: Applications Of ML In the Dental Oral And Craniofacial Imaging Fieldmentioning

confidence: 99%

Machine learning in dental, oral and craniofacial imaging: a review of recent progress

Ren

Luo

et al. 2021

PeerJ

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“…The calculation volume required to process three-dimensional piles of images, based on spatial or time axes, is incomparable to that required for two-dimensional images. One solution is to compress the images [Kang et al, 2018] and input them to deep learning, but the compression process results in the loss of detailed information. In this study, we applied the method of multi-phase deep learning, used to predict landmarks in 2D cephalograms [Nishimoto, 2020][Nishimoto et al, 2020, to 3D craniofacial images.…”

Section: Multi-phased Deep Learningmentioning

confidence: 99%

Three-dimensional cranio-facial landmark detection in CT slices from a publicly available database, using multi-phased regression networks on a personal computer

Nishimoto

Saito

Ishise

et al. 2021

Preprint

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Aim: Geometrical assessments to comprehend the shape of an object are done based on characteristic landmarks. Computer assisted tomography (CT) images, horizontal slices as two-dimensional pictures, can be digitally restructured into virtual three-dimensional objects. Automatic detection of the landmarks, if developed, will be a great help not only medically, but also for anthropologically. The aim of this study is to develop an automated system to predict three-dimensional coordinate values of cranio-facial landmarks in sequences of CT slices. Methods: CT images were obtained from a publicly available database. Digital reconstruction was done to obtain three dimensional models. Sixteen landmarks were plotted on the models and coordinate values of them were recorded. Multi-phased deep learning system was constructed. For the first phase, 512 x 512 pixels images were resized to 96 x 96 pixels. A regression deep learning network was trained with 90 training data. For the second phase, for each landmark, 100 x 100 pixels images were cropped from the original images. Sixteen models were trained. For the third phase, 50 x 50 pixels images were cropped, and models were trained. Results: Three-dimensional error for the first phase, testing 30 data, was 11.60 pixels in average. (1 pixel = 500 / 512 mm) For the second phase, it was significantly improved to 4.66 pixels. For the third phase, it was significantly progressed to 2.89. This was comparable to the gaps between the landmarks, plotted by two experienced practitioners. Discussion: The calculation volume required to process three-dimensional pile of images is tremendous. One solution may be to compress the images, but detailed information may be lost during the process. Our proposing method of multi-phased prediction, coarse detection first and narrowing down the detection area, may be a possible solution, within the physical limitation of memory and computation.

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“…For 3D CT scans Lee et al [11] proposed VGG-based [12] method for detecting landmarks on shadowed 2D projections. A completely 3D based approach using 3D convolutional neural network based system was proposed by Kang et al [13].…”

Section: Related Workmentioning

confidence: 99%

DL-inferencing for 3D Cephalometric Landmarks Regression task using OpenVINO

Vasiliev

Lachinov

Getmanskaya

2020

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2

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In this paper, we evaluate the performance of the Intel Distribution of OpenVINO toolkit in practical solving of the problem of automatic three-dimensional Cephalometric analysis using deep learning methods. This year, the authors proposed an approach to the detection of cephalometric landmarks from CT-tomography data, which is resistant to skull deformities and use convolutional neural networks (CNN). Resistance to deformations is due to the initial detection of 4 points that are basic for the parameterization of the skull shape. The approach was explored on CNN for three architectures. A record regression accuracy in comparison with analogs was obtained. This paper evaluates the perfor- mance of decision making for the trained CNN-models at the inference stage. For a comparative study, the computing environments PyTorch and Intel Distribution of OpenVINO were selected, and 2 of 3 CNN architectures: based on VGG for regression of cephalometric landmarks and an Hourglass-based model, with the RexNext backbone for the land- marks heatmap regression. The experimental dataset was consist of 20 CT of patients with acquired craniomaxillofacial deformities and was in- clude pre- and post-operative CT scans whose format is 800x800x496 with voxel spacing of 0.2x0.2x0.2 mm. Using OpenVINO showed a great increase in performance over the PyTorch, with inference speedup from 13 to 16 times for a Direct Regression model and from 3.5 to 3.8 times for a more complex and precise Hourglass model.

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

Cited by 22 publications

References 46 publications

Machine learning in dental, oral and craniofacial imaging: a review of recent progress

Machine learning in dental, oral and craniofacial imaging: a review of recent progress

Three-dimensional cranio-facial landmark detection in CT slices from a publicly available database, using multi-phased regression networks on a personal computer

DL-inferencing for 3D Cephalometric Landmarks Regression task using OpenVINO

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