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Scale- and Slice-aware Net (S2 aNet) for 3D segmentation of organs and musculoskeletal structures in pelvic MRI.

Chaoyang YanJing-Jing LuKang ChenLei WangHaoda LuLi YuMengyan SunJun Xu
Published in: Magnetic resonance in medicine (2021)
The experimental results on the pelvic 3D MR dataset show that the proposed S 2 aNet achieves excellent segmentation results compared to other state-of-the-art models. To our knowledge, S 2 aNet is the first model to achieve 3D dense segmentation for 54 musculoskeletal structures on pelvic MRI, which will be leveraged to the clinical application under the support of more cases in the future.
Keyphrases
  • deep learning
  • contrast enhanced
  • convolutional neural network
  • rectal cancer
  • magnetic resonance imaging
  • diffusion weighted imaging
  • healthcare
  • magnetic resonance