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<p>Accurate segmentation of pulmonary nodules in low-dose CT (LDCT) is vital for early lung cancer detection. Existing voxel-based methods often fail to capture irregular nodule boundaries, especially under noisy, low-contrast conditions. We propose ShapeField-Nodule, a continuous shape embedding framework that models nodule geometry as a signed distance field (SDF), enabling sub-voxel precision and anatomically coherent contours. Our method integrates a lightweight MLP-based implicit head with a 3D U-Net backbone to predict dense SDF values, and introduces a shape-aware refinement loss that aligns SDF gradients with image edges. Unlike discrete masks, our representation enforces boundary smoothness, topology regularization, and robustness to perturbations. Evaluations on LIDC-IDRI, LUNA16, and Tianchi datasets show state-of-the-art Dice and surface metrics. Extensive experiments demonstrate superior generalization, robustness under noise, and inference efficiency, highlighting the potential of continuous implicit fields as a principled alternative for medical image segmentation.</p>

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ShapeField-lung: continuous shape embedding for early lung cancer detection via pulmonary nodule segmentation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月27日

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  • Xuyu Gu,
  • Yifei Zhu,
  • Chuangqi Li,
  • Xinnan Xu,
  • Kaiqi Jin,
  • Li Xu

摘要

Accurate segmentation of pulmonary nodules in low-dose CT (LDCT) is vital for early lung cancer detection. Existing voxel-based methods often fail to capture irregular nodule boundaries, especially under noisy, low-contrast conditions. We propose ShapeField-Nodule, a continuous shape embedding framework that models nodule geometry as a signed distance field (SDF), enabling sub-voxel precision and anatomically coherent contours. Our method integrates a lightweight MLP-based implicit head with a 3D U-Net backbone to predict dense SDF values, and introduces a shape-aware refinement loss that aligns SDF gradients with image edges. Unlike discrete masks, our representation enforces boundary smoothness, topology regularization, and robustness to perturbations. Evaluations on LIDC-IDRI, LUNA16, and Tianchi datasets show state-of-the-art Dice and surface metrics. Extensive experiments demonstrate superior generalization, robustness under noise, and inference efficiency, highlighting the potential of continuous implicit fields as a principled alternative for medical image segmentation.