Work

EDC-UNet: A Pulmonary Nodule Segmentation Method Based on Edge Detail Enhancement

Year: 2025

Type: article

Abstract: Precise and automatic segmentation of pulmonary lesions is crucial for assisting pulmonologists in accurate diagnosis and decision-making. Despite advances in deep learning, segmenting pulmonary nodules remains challenging due to factors like small lesions, irregular boundaries, and data imbalance. We propose an edge detail enhancement method (EDC-UNet) for pulmonary nodule segmentation, which integrates deformable convolutional layers to improve flexibility for various lesion morphologies and dilated convolution-based residual blocks to enhance feature extraction. Additionally, a Sobel-based detail supervision module in the decoder helps capture low-level spatial details, improving segmentation of blurred edges. Extensive experiments on the LIDC-IDRI and LUNA16 datasets demonstrate that EDC-UNet outperforms other models, highlighting its potential for medical image analysis.

Source: IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences

DOI: 10.1587/transfun.2025eal2036

Authors: Bowen LIU, Hongbo ZHU, Wenbo ZHANG, Yuanguo BI, Qi QI

Institutions: Shenyang Ligong University, Northeastern University, Northeastern 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: bronze

Funder: National Natural Science Foundation of China