Purpose: To develop a fully automated, accurate and robust segmentation technique for dental implants on cone-beam CT (CBCT) images.
Sep 29, 2013 · Purpose. To develop a fully automated, accurate and robust segmentation technique for dental implants on cone-beam CT (CBCT) images.
Sep 9, 2013 · To develop a fully automated, accurate and robust segmentation technique for dental implants on cone-beam CT (CBCT) images.
Automated implant segmentation in cone-beam CT using edge detection and particle counting. https://doi.org/10.1007/s11548-013-0946-z.
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The proposed cloud-based deep learning tool demonstrated high performance and time-efficient segmentation of implants on CBCT images.
Apr 25, 2024 · Automated implant segmentation in cone-beam CT using edge detection and particle counting. Int. J. Comput. Assist. Radiol. Surg. 9(4): 733 ...
Oct 22, 2024 · Objectives: To train and validate a cloud-based convolutional neural network (CNN) model for automated segmentation (AS) of dental implant ...
Aug 6, 2024 · This study aims to segment metal implants in CT images using a diffusion model and further validate it with clinical artifact images and phantom ...
Automated implant segmentation in cone-beam CT using edge detection and particle counting. Ruben Pauwels; Reinhilde Jacobs; Soontra Panmekiate. Original Article ...
Automated implant segmentation in cone-beam CT using edge detection and particle counting. Int. J. Comput. Assist. Radiol. Surg., 9 (2014), pp. 733-743 ...