Comparison of 2D, 2.5D, and 3D segmentation networks for mandibular canals in CBCT images: A study on public and external datasets
Su Yang,
Jong Soo Jeong,
Dahyun Song
et al.
Abstract:The purpose of this study was to compare the performances of 2D, 2.5D, and 3D CNN-based segmentation networks, along with a 3D vision transformer-based segmentation network, for segmenting mandibular canals (MCs) on the public and external CBCT datasets under the same GPU memory capacity. We also performed ablation studies for an image-cropping (IC) technique and segmentation loss functions. 3D-UNet showed the highest segmentation performance for the MC than those of 2D and 2.5D segmentation networks on public… Show more
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