<scp>CCEMSS</scp> ‐Unet++: An Enhanced Multi‐Scale Context Fusion Network for Pulmonary Nodule Segmentation
Zhen Cui, Qing Lu, Xia Wang
Abstract
ABSTRACT In CT imaging, the size, shape, margin, and density of pulmonary nodules are used to determine whether they are benign or malignant. However, pulmonary nodules often exhibit challenging characteristics in CT images, such as irregular shapes, and tiny nodules are prone to being overlooked. In addition, the density of nodules may be similar to that of surrounding tissues. When the nodules are small or close to the pulmonary wall, their resolution is low, making them difficult to distinguish. Because of these characteristics, it is quite challenging to segment pulmonary nodules automatically. This research proposes CCEMSS‐Unet++, a medical image segmentation network that enhances feature fusion between local details and global context through a multi‐scale design. It includes the CCEMS module and the SE attention mechanism. The SE module mitigates false negatives, particularly for small nodules; the CCEMS module is used to extract local information and connect global context, improving segmentation accuracy. This study used two datasets: the publicly available LIDC‐IDRI dataset and a dataset collected from the CT Department of Yan'an University Affiliated Hospital. Compared with Unet++, the proposed model improves IoU and Dice by 2.62% and 2.15% on the LIDC‐IDRI dataset, and by 5.10% and 7.11% on our dataset, respectively. In terms of segmentation accuracy and generalization, the experimental findings demonstrate that this approach outperforms other networks, including handling nodules with low resolution. The efficacy of the proposed enhancement method is further supported by the ablation experiments.