Frequency-enhanced Multi-granularity Context Network for Efficient Vertebrae Segmentation
Journal:
arXiv
Published Date:
Jun 29, 2025
Abstract
Automated and accurate segmentation of individual vertebra in 3D CT and MRI
images is essential for various clinical applications. Due to the limitations
of current imaging techniques and the complexity of spinal structures, existing
methods still struggle with reducing the impact of image blurring and
distinguishing similar vertebrae. To alleviate these issues, we introduce a
Frequency-enhanced Multi-granularity Context Network (FMC-Net) to improve the
accuracy of vertebrae segmentation. Specifically, we first apply wavelet
transform for lossless downsampling to reduce the feature distortion in blurred
images. The decomposed high and low-frequency components are then processed
separately. For the high-frequency components, we apply a High-frequency
Feature Refinement (HFR) to amplify the prominence of key features and filter
out noises, restoring fine-grained details in blurred images. For the
low-frequency components, we use a Multi-granularity State Space Model (MG-SSM)
to aggregate feature representations with different receptive fields,
extracting spatially-varying contexts while capturing long-range dependencies
with linear complexity. The utilization of multi-granularity contexts is
essential for distinguishing similar vertebrae and improving segmentation
accuracy. Extensive experiments demonstrate that our method outperforms
state-of-the-art approaches on both CT and MRI vertebrae segmentation datasets.
The source code is publicly available at https://github.com/anaanaa/FMCNet.
