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

• URL: http://arxiv.org/abs/2102.12755v1
• Date: Thu, 25 Feb 2021 09:51:30 GMT
• Title: Coarse-to-fine Airway Segmentation Using Multi information Fusion Network and CNN-based Region Growing
• Authors: Jinquan Guo, Rongda Fu, Lin Pan, Shaohua Zheng, Liqin Huang, Bin Zheng, Bingwei He
• Abstract summary: Low contrast at peripheral branches and complex tree-like structures remain as two main challenges for airway segmentation. Recent research has illustrated that deep learning methods perform well in segmentation tasks. A coarse-to-fine segmentation framework is proposed to obtain a complete airway tree.
• Score: 6.357411132360318
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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