The Effectiveness of Semi-Automated and Fully Automatic Segmentation for Inferior Alveolar Canal Localization on CBCT Scans: A Systematic Review

Issa, Julien; Olszewski, Raphaël; Dyszkiewicz-Konwińska, Marta

doi:10.3390/ijerph19010560

Cited by 17 publications

(17 citation statements)

References 55 publications

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“…A systematic review was conducted by Issa et al [ 18 ] to investigate AI methods used for the specific task of detecting the IAC within CBCT images. The authors concluded that CBCT 3D images allowed practitioners a comprehensive view of the IAC, and that the lack of uniform reporting of the methodology and results affected the quality of the published work.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of the as Low as Diagnostically Acceptable CT Dose for Identification of the Inferior Alveolar Canal Using 3D Convolutional Neural Networks with Multi-Balancing Strategies

et al. 2023

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Ionizing radiation is necessary for diagnostic imaging and deciding the right radiation dose is extremely critical to obtain a decent quality image. However, increasing the dosage to improve the image quality has risks due to the potential harm from ionizing radiation. Thus, finding the optimal as low as diagnostically acceptable (ALADA) dosage is an open research problem that has yet to be tackled using artificial intelligence (AI) methods. This paper proposes a new multi-balancing 3D convolutional neural network methodology to build 3D multidetector computed tomography (MDCT) datasets and develop a 3D classifier model that can work properly with 3D CT scan images and balance itself over the heavy unbalanced multi-classes. The proposed models were exhaustively investigated through eighteen empirical experiments and three re-runs for clinical expert examination. As a result, it was possible to confirm that the proposed models improved the performance by an accuracy of 5% to 10% when compared to the baseline method. Furthermore, the resulting models were found to be consistent, and thus possibly applicable to different MDCT examinations and reconstruction techniques. The outcome of this paper can help radiologists to predict the suitability of CT dosages across different CT hardware devices and reconstruction algorithms. Moreover, the developed model is suitable for clinical application where the right dose needs to be predicted from numerous MDCT examinations using a certain MDCT device and reconstruction technique.

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Section: Literature Reviewmentioning

confidence: 99%

Prediction of the as Low as Diagnostically Acceptable CT Dose for Identification of the Inferior Alveolar Canal Using 3D Convolutional Neural Networks with Multi-Balancing Strategies

et al. 2023

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“…Several efforts have been made to develop semi-automated or fully automated solutions for automatic segmentation of mandibular canal [11]. Based on the techniques utilized for development, these systems can be classified into two categories, i.e., classical image processing-based methods and advanced deep learning-based techniques.…”

Section: Related Workmentioning

confidence: 99%

“…Several studies have attempted to overcome the challenges mentioned above by developing various systems for automatic segmentation of the mandibular canal in CBCT scans. Such systems include classical image processing-based techniques and advanced deep learning-based methods [11]. Classical methods mostly rely on raw voxel values and consider 2D contextual information to determine the mandibular canal position.…”

Section: Introductionmentioning

confidence: 99%

Dual-Stage Deeply Supervised Attention-Based Convolutional Neural Networks for Mandibular Canal Segmentation in CBCT Scans

Usman

Rehman

Saleem

et al. 2022

Sensors

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Accurate segmentation of mandibular canals in lower jaws is important in dental implantology. Medical experts manually determine the implant position and dimensions from 3D CT images to avoid damaging the mandibular nerve inside the canal. In this paper, we propose a novel dual-stage deep learning-based scheme for the automatic segmentation of the mandibular canal. In particular, we first enhance the CBCT scans by employing the novel histogram-based dynamic windowing scheme, which improves the visibility of mandibular canals. After enhancement, we designed 3D deeply supervised attention UNet architecture for localizing the Volumes Of Interest (VOIs), which contain the mandibular canals (i.e., left and right canals). Finally, we employed the Multi-Scale input Residual UNet (MSiR-UNet) architecture to segment the mandibular canals using VOIs accurately. The proposed method has been rigorously evaluated on 500 and 15 CBCT scans from our dataset and from the public dataset, respectively. The results demonstrate that our technique improves the existing performance of mandibular canal segmentation to a clinically acceptable range. Moreover, it is robust against the types of CBCT scans in terms of field of view.

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“…The emergence of CBCT has greatly improved the diagnostic success rate of difficult oral cases and promoted the development of oral medicine. Compared with spiral CT, CBCT has the advantages of small voxel, high image spatial resolution, short scanning time, and low radiation dose, so it is widely used in clinical practice [47][48][49][50][51].…”

Section: The Application Of Cbct In Root Canal Therapymentioning

confidence: 99%

Research progress of mandibular first molar preparation with nickel-titanium instruments under the guidance of CBCT

Zhang¹,

Zhong-yu²

2022

IJASR

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: Endodontic disease or periapical disease is one of the most common oral diseases in clinic. Root canal therapy (RCT) is currently the most common and effective method for the treatment of endodontic or periapical diseases. Root canal therapy (RCT) is currently the most common and effective method for the treatment of endodontic or periapical diseases. The complex root canal morphology of mandibular first molar is a major challenge for its root canal treatment. The application of nickel-titanium instruments has brought new breakthroughs in root canal therapy, but also brought some new hidden dangers. This article reviews the research status of cone beam computed tomography (CBCT) guided nickel-titanium instruments for mandibular first molar preparation in recent years.

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The Effectiveness of Semi-Automated and Fully Automatic Segmentation for Inferior Alveolar Canal Localization on CBCT Scans: A Systematic Review

Cited by 17 publications

References 55 publications

Prediction of the as Low as Diagnostically Acceptable CT Dose for Identification of the Inferior Alveolar Canal Using 3D Convolutional Neural Networks with Multi-Balancing Strategies

Prediction of the as Low as Diagnostically Acceptable CT Dose for Identification of the Inferior Alveolar Canal Using 3D Convolutional Neural Networks with Multi-Balancing Strategies

Dual-Stage Deeply Supervised Attention-Based Convolutional Neural Networks for Mandibular Canal Segmentation in CBCT Scans

Research progress of mandibular first molar preparation with nickel-titanium instruments under the guidance of CBCT

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