Work

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

Year: 2025

Type: article

Abstract: 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.

Source: npj Digital Medicine

DOI: 10.1038/s41746-025-02041-y

Authors: Xuyu Gu, Yifei Zhu, Chuangqi Li, Xinnan Xu, Kaiqi Jin, Li Xu

Institutions: Shanghai Pulmonary Hospital, Shanghai Medical College of Fudan University, Fudan University Shanghai Cancer Center, Tsinghua University

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $4,090