Optimized IANSegNet: Deep Segmentation for the Detection of Inferior Alveolar Nerve Canal.
Imaging studies in dentistry and maxillofacial pathology have recently concentrated on detecting the inferior alveolar nerve (IAN) canal. In spite of the minor dimensions of 3D maxillofacial datasets, deep learning-based algorithms have shown encouraging consequences in this study area. This study d...
| Publicado en: | BioMed Research International pp. 1 - 11 |
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| Autores principales: | , , , , , , , , |
| Formato: | computer program diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
4/28/2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=163483652&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 163483652 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 4/28/2023 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 163483652 163483652 163483652 10.1155/2023/6431692 163483652 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Optimized IANSegNet: Deep Segmentation for the Detection of Inferior Alveolar Nerve Canal. aug: au: Krishnan, V. Gokula Navaneethakrishnan, M. Ganesan, Sangeetha Saradhi, M. V. Vijaya Hemapriya, K. Selvaraj, D. Deepa, J. Murthy, K. Sreerama Doss, Srinath affil: Department of CSE, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai, Tamil Nadu, India sug: subj: Mandibular Nerve Mandibular Canal Mandibular Diseases Diagnosis Deep Learning Mandible Radiography Tomography, X-Ray Computed Methods Minimum Data Set Mandible Pathology Neural Networks (Computer) Human Image Processing, Computer Assisted Validation Studies ab: Imaging studies in dentistry and maxillofacial pathology have recently concentrated on detecting the inferior alveolar nerve (IAN) canal. In spite of the minor dimensions of 3D maxillofacial datasets, deep learning-based algorithms have shown encouraging consequences in this study area. This study describes a mandibular cone-beam CT (CBCT) dataset with 2D and 3D hand comments. It is huge and freely available. It was possible to utilise this dataset by applying the residual neural network (IANSegNet), which consumed less GPU memory and computational complexity. As an encoder, IANSegNet uses the computationally efficient 3D ShuffleNetV2 network to reduce graphics processing unit (GPU) memory usage and improve efficiency. After that, a decoder with leftover blocks is added to keep the quality high. To address network convergence and data inequity, Dice's loss and cross-entropy loss were created. Optimized postprocessing techniques are also recommended for fine-tuning the coarse segmentation findings that are generated by IANSegNet. The results of the validation show that IANSegNet outperformed other deep learning models in a variety of criteria. pubtype: Academic Journal doctype: computer program diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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