A dual‐attention V‐network for pulmonary lobe segmentation in CT scans

Shaohua Zheng; Weiyu Nie; Lin Pan; Bin Zheng; Zhiqiang Shen; Lingling Huang; Chenhao Pei; Yuhang She; liuqing Chen

doi:10.1049/ipr2.12133

journal article Open Access Feb 17, 2021

A dual‐attention V‐network for pulmonary lobe segmentation in CT scans

Shaohua Zheng Weiyu Nie Lin Pan

Bin Zheng

Zhiqiang Shen

Lingling Huang

Chenhao Pei Yuhang She liuqing Chen

IET Image Processing Vol. 15 No. 8 pp. 1644-1654 · Institution of Engineering and Technology (IET)

View at Publisher Save 10.1049/ipr2.12133

Abstract

Abstract
The reliable and automatic segmentation of pulmonary lobes in computed tomography scans is an important pre‐condition for the diagnosis, assessment, and treatment of lung diseases. However, due to the incomplete lobar structures and morphological changes caused by diseases, the lobe segmentation still encounters great challenges. Recently, convolution neural network has exerted a tremendous impact on medical image analysis. Nevertheless, the basic convolution operations mainly obtain local features that are insufficient for accurate lobe segmentation. The idea that the global features are equally crucial especially when lesions appear is considered. Here, a dual‐attention V‐network named DAV‐Net for pulmonary lobe segmentation is proposed. First, a novel dual‐attention module to capture global contextual information and model the semantic dependencies in spatial and channel dimensions is introduced. Second, a progressive output scheme is used to avoid the vanishing gradient phenomenon and obtain relatively effective features in hidden layers. Finally, an improved combo loss is devised to address input and output lobe imbalance problem during training and inference. In the evaluation using the LUNA16 dataset and our in‐house dataset, the proposed DAV‐Net obtains Dice similarity coefficients of 0.947 and 0.934, respectively; these values are superior to those obtained by existing methods.

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Details

Published: Feb 17, 2021
Vol/Issue: 15(8)
Pages: 1644-1654
License: View

Authors

S

Shaohua Zheng