mlsanityData to 13 Oct 2026

FDNet: Feature Decoupled Segmentation Network for Tooth CBCT Image

arXiv 2311.06551 ↗First posted 11 Nov 2023cs.CVcited by 1

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Abstract (arXiv)

Precise Tooth Cone Beam Computed Tomography (CBCT) image segmentation is crucial for orthodontic treatment planning. In this paper, we propose FDNet, a Feature Decoupled Segmentation Network, to excel in the face of the variable dental conditions encountered in CBCT scans, such as complex artifacts and indistinct tooth boundaries. The Low-Frequency Wavelet Transform (LF-Wavelet) is employed to enrich the semantic content by emphasizing the global structural integrity of the teeth, while the SAM encoder is leveraged to refine the boundary delineation, thus improving the contrast between adjacent dental structures. By integrating these dual aspects, FDNet adeptly addresses the semantic gap, providing a detailed and accurate segmentation. The framework's effectiveness is validated through rigorous benchmarks, achieving the top Dice and IoU scores of 85.28% and 75.23%, respectively. This innovative decoupling of semantic and boundary features capitalizes on the unique strengths of each element to elevate the quality of segmentation performance.

Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 9 of its 11 references that are in the map. First posted on arXiv 11 Nov 2023.

Builds on

9 of 11 references.

References · 9

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,656
  2. 2023Segment Anythingcited by 8,195
  3. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,522
  4. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,429
  5. 2020Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformerscited by 1,369
  6. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,219
  7. 2021Medical Transformer: Gated Axial-Attention for Medical Image Segmentationcited by 320
  8. 2021UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformercited by 197
  9. 2021GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentationcited by 20

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  1. 2024Accelerating Parallel Sampling of Diffusion Modelscited by 30