Coarse-to-fine Airway Segmentation Using Multi information Fusion Network and CNN-based Region Growing

TL;DR

This paper introduces a coarse-to-fine airway segmentation framework combining multi-information fusion CNN and CNN-based region growing to improve the accuracy of airway tree segmentation from CT scans, addressing challenges of low contrast and complex structures.

Contribution

It proposes a novel multi-information fusion CNN with ASPP for coarse segmentation and a CNN-based region growing for fine details, enhancing airway segmentation accuracy.

Findings

Effective segmentation of complete airway trees achieved

Improved detection of small airway branches

Enhanced accuracy over existing methods

Abstract

Automatic airway segmentation from chest computed tomography (CT) scans plays an important role in pulmonary disease diagnosis and computer-assisted therapy. However, low contrast at peripheral branches and complex tree-like structures remain as two mainly challenges for airway segmentation. Recent research has illustrated that deep learning methods perform well in segmentation tasks. Motivated by these works, a coarse-to-fine segmentation framework is proposed to obtain a complete airway tree. Our framework segments the overall airway and small branches via the multi-information fusion convolution neural network (Mif-CNN) and the CNN-based region growing, respectively. In Mif-CNN, atrous spatial pyramid pooling (ASPP) is integrated into a u-shaped network, and it can expend the receptive field and capture multi-scale information. Meanwhile, boundary and location information are incorporated into semantic information. These information are fused to help Mif-CNN utilize additional context knowledge and useful features. To improve the performance of the segmentation result, the CNN-based region growing method is designed to focus on obtaining small branches. A voxel classification network (VCN), which can entirely capture the rich information around each voxel, is applied to classify the voxels into airway and non-airway. In addition, a shape reconstruction method is used to refine the airway tree.