Residual Fully Convolutional Network for Mandibular Canal Segmentation

doi:10.22266/ijies2021.1231.20

Cited by 5 publications

(5 citation statements)

References 22 publications

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“…Residual Fully Convolutional Network (RFCN) is a deep learning architecture that combines the U-Net architecture with residual units to improve performance in medical image segmentation tasks. This approach has already been applied for MC segmentation in 2D CBCT [65]. By using RFCN, the model is able to segment the MC accurately.…”

Section: ) Rfcnmentioning

confidence: 99%

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Deep Learning for Mandibular Canal Segmentation in Digital Dental Radiographs: A Systematic Literature Review

Farid Naufal,

Fatichah,

Renwi Astuti

et al. 2024

IEEE Access

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Context: Several recent studies in dentistry and maxillofacial imaging have concentrated on the mandibular canal (MC) segmentation in digital dental radiographs. In this research domain, deep learning approaches have demonstrated promising outcomes. Objective: This systematic literature review (SLR) aims to comprehensively analyze and synthesize the recent advancements in applying deep learning techniques for MC segmentation in digital dental radiographs. Method: This review encompasses studies published between 2018 and 2023, sourced from reputable databases, including PubMed, ScienceDirect, IEEE Xplore, and Google Scholar. Results: This study identified 30 primary research papers focusing on MC segmentation in digital dental radiographs. The review categorizes papers into two groups based on the digital dental radiograph type. The first group uses a 2D digital dental radiograph from a panoramic radiograph and 2D Cone Beam Computed Tomography (CBCT) scans. The second group uses 3D datasets from volumetric data from CBCT scans. Conclusion:The synthesized knowledge from this review is intended to guide researchers, dentists, and oral surgeons in leveraging deep learning advancements in MC segmentation for oral and maxillofacial surgery. Prior studies have faced challenges, including limited public datasets, variations in MC anatomy, time consumption and observer variability in MC annotation, complexities in deep learning models, and lack of practical implementation. To overcome these challenges, it is suggested that more public datasets be collaboratively created and shared within the research community, focusing on handling anatomy variability, improving digital dental radiograph quality, streamlining annotation processes through automated tools, and simplifying deep learning models for practical implementation.

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Section: ) Rfcnmentioning

confidence: 99%

“…Faradhilla et al [65] 2D CBCT This study introduces fully convolutional network (RFCN), which takes into account the loss values in both the region and boundary of MC segmentation. To enhance the performance of the RFCN, dual auxiliary loss (DAL) functions are incorporated, optimizing the network for improved object segmentation.…”

Section: Remarksmentioning

confidence: 99%

Deep Learning for Mandibular Canal Segmentation in Digital Dental Radiographs: A Systematic Literature Review

Farid Naufal,

Fatichah,

Renwi Astuti

et al. 2024

IEEE Access

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“…However, this method necessitates some manual input and struggles with samples that have indistinct mandibular canal boundaries. To counteract the issue of blurred boundaries, Faradhilla et al (2021) introduced a Double Auxiliary Loss (DAL) in the loss function to make the network more attentive to the target area and its boundaries, achieving a high Dice accuracy of 0.914 on their private dataset. To combat class imbalance, Du et al (2022) innovatively introduced a pre-processing step involving centerline extraction and region growing to identify the mandibular canal’s location.…”

Section: Related Workmentioning

confidence: 99%

Automatic segmentation of mandibular canal using transformer based neural networks

Lv,

Zhang,

et al. 2023

Front. Bioeng. Biotechnol.

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Accurate 3D localization of the mandibular canal is crucial for the success of digitally-assisted dental surgeries. Damage to the mandibular canal may result in severe consequences for the patient, including acute pain, numbness, or even facial paralysis. As such, the development of a fast, stable, and highly precise method for mandibular canal segmentation is paramount for enhancing the success rate of dental surgical procedures. Nonetheless, the task of mandibular canal segmentation is fraught with challenges, including a severe imbalance between positive and negative samples and indistinct boundaries, which often compromise the completeness of existing segmentation methods. To surmount these challenges, we propose an innovative, fully automated segmentation approach for the mandibular canal. Our methodology employs a Transformer architecture in conjunction with cl-Dice loss to ensure that the model concentrates on the connectivity of the mandibular canal. Additionally, we introduce a pixel-level feature fusion technique to bolster the model’s sensitivity to fine-grained details of the canal structure. To tackle the issue of sample imbalance and vague boundaries, we implement a strategy founded on mandibular foramen localization to isolate the maximally connected domain of the mandibular canal. Furthermore, a contrast enhancement technique is employed for pre-processing the raw data. We also adopt a Deep Label Fusion strategy for pre-training on synthetic datasets, which substantially elevates the model’s performance. Empirical evaluations on a publicly accessible mandibular canal dataset reveal superior performance metrics: a Dice score of 0.844, click score of 0.961, IoU of 0.731, and HD95 of 2.947 mm. These results not only validate the efficacy of our approach but also establish its state-of-the-art performance on the public mandibular canal dataset.

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“…Before the feature extraction uses geometric features so the image is binary (black and white) to extract the shapes of the image object, there is a segmentation stage. Previous studies related to segmentation included segmentation of the mandibular canal on radiographs [11] and segmentation of each tooth [12]. However, under certain conditions, teeth cannot be used as an identification tool for several reasons.…”

Section: Introductionmentioning

confidence: 99%

Automatic for Generating Landmark Mandibular Panoramic Radiography Image

2024

IJIES

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Forensic gender identification based on teeth is needed to identify incomplete victims, only bones and teeth. Previous studies identified gender by manually or semi-automatically measuring mandibular parameters based on landmark points. Gender identification, especially for victims of mass disasters, requires accuracy, so it takes longer, especially if there are many parameters to be measured. In addition, the observer's manual or semi-automatic measurements may give different results. This study proposes a new automatic approach to generate ten mandibular landmark points from panoramic radiographic images for gender identification. We propose a step, namely determining the centroid point of the mandibular image and using linear regression to predict ten mandibular landmark points such as the two condyle, two gonion, four ramus, and two body of the mandible. This study obtained panoramic radiographic images from the Academic Dental Hospital, Universitas Airlangga. We calculated the distance between expert mandibular landmark points and the predicted results to evaluate performance. The prediction of the landmark with the smallest average distance is the lower point of the mandibular body 1 pixel. In comparison, the predicted landmark with the most extended average length is a gonion of 10 pixels.

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Residual Fully Convolutional Network for Mandibular Canal Segmentation

Cited by 5 publications

References 22 publications

Deep Learning for Mandibular Canal Segmentation in Digital Dental Radiographs: A Systematic Literature Review

Deep Learning for Mandibular Canal Segmentation in Digital Dental Radiographs: A Systematic Literature Review

Automatic segmentation of mandibular canal using transformer based neural networks

Automatic for Generating Landmark Mandibular Panoramic Radiography Image

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