Airway Segmentation Network for Enhanced Tubular Feature Extraction

TL;DR

This paper introduces TfeNet, a novel neural network with direction-aware convolution and tubular feature fusion for improved automatic airway segmentation in CT images, addressing challenges of fine structure detection.

Contribution

The study proposes TfeNet with a new direction-aware convolution and feature fusion modules, enhancing airway segmentation accuracy and continuity over existing methods.

Findings

Achieves 94.95% accuracy on ATM22 dataset.

Outperforms existing methods in airway structure continuity.

Demonstrates superior performance on lung fibrosis dataset.

Abstract

Manual annotation of airway regions in computed tomography images is a time-consuming and expertise-dependent task. Automatic airway segmentation is therefore a prerequisite for enabling rapid bronchoscopic navigation and the clinical deployment of bronchoscopic robotic systems. Although convolutional neural network methods have gained considerable attention in airway segmentation, the unique tree-like structure of airways poses challenges for conventional and deformable convolutions, which often fail to focus on fine airway structures, leading to missed segments and discontinuities. To address this issue, this study proposes a novel tubular feature extraction network, named TfeNet. TfeNet introduces a novel direction-aware convolution operation that first applies spatial rotation transformations to adjust the sampling positions of linear convolution kernels. The deformed kernels are then represented as line segments or polylines in 3D space. Furthermore, a tubular feature fusion module (TFFM) is designed based on asymmetric convolution and residual connection strategies, enhancing the network's focus on subtle airway structures. Extensive experiments conducted on one public dataset and two datasets used in airway segmentation challenges demonstrate that the proposed TfeNet achieves more accuracy and continuous airway structure predictions compared with existing methods. In particular, TfeNet achieves the highest overall score of 94.95% on the current largest airway segmentation dataset, Airway Tree Modeling(ATM22), and demonstrates advanced performance on the lung fibrosis dataset(AIIB23). The code is available at https://github.com/QibiaoWu/TfeNet.