ArticleBioengineering (Basel, Switzerland)2024
Inferior Alveolar Nerve Canal Segmentation on CBCT Using U-Net with Frequency Attentions.
Article in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 10 citations in OpenAlex.
- Transformer and generative models in dental and maxillofacial imaging: Narrative review of clinical evidence and future directions.Imaging science in dentistry · 2026Review
- Evaluation of the Effects of Implants with Different Geometries on the Inferior Alveolar Nerve Under Occlusal Stresses.Journal of functional biomaterials · 2026Article
- Clinically applicable deep learning model for segmentation of the mandibular bone and inferior alveolar canal in CBCT cross-sectional images.BMC oral health · 2026Article
- [Research and implementation of intelligent diagnostic system for temporomandibular joint disorder].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Accurate inferior alveolar nerve (IAN) canal segmentation has been considered a crucial task in dentistry. Failing to accurately identify the position of the IAN canal may lead to nerve injury during dental procedures. While IAN canals can be detected from dental cone beam computed tomography, they are usually difficult for dentists to precisely identify as the canals are thin, small, and span across many slices. This paper focuses on improving accuracy in segmenting the IAN canals. By integrating our proposed frequency-domain attention mechanism in UNet, the proposed frequency attention UNet (FAUNet) is able to achieve 75.55% and 81.35% in the Dice and surface Dice coefficients, respectively, which are much higher than other competitive methods, by adding only 224 parameters to the classical UNet. Compared to the classical UNet, our proposed FAUNet achieves a 2.39% and 2.82% gain in the Dice coefficient and the surface Dice coefficient, respectively. The potential advantage of developing attention in the frequency domain is also discussed, which revealed that the frequency-domain attention mechanisms can achieve better performance than their spatial-domain counterparts.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.