Paper Info
Title
Alleviating Class-Wise Gradient Imbalance for Pulmonary Airway Segmentation
Abstract
Automated airway segmentation is a prerequisite for pre-operative diagnosis and intra-operative navigation for pulmonary intervention. Due to the small size and scattered spatial distribution of peripheral bronchi, this is hampered by a severe class imbalance between foreground and background regions, which makes it challenging for CNN-based methods to parse distal small airways. In this paper, we demonstrate that this problem is arisen by gradient erosion and dilation of the neighborhood voxels. During back-propagation, if the ratio of the foreground gradient to background gradient is small while the class imbalance is local, the foreground gradients can be eroded by their neighborhoods. This process cumulatively increases the noise information included in the gradient flow from top layers to the bottom ones, limiting the learning of small structures in CNNs. To alleviate this problem, we use group supervision and the corresponding WingsNet to provide complementary gradient flows to enhance the training of shallow layers. To further address the intra-class imbalance between large and small airways, we design a General Union loss function that obviates the impact of airway size by distance-based weights and adaptively tunes the gradient ratio based on the learning process. Extensive experiments on public datasets demonstrate that the proposed method can predict the airway structures with higher accuracy and better morphological completeness than the baselines.
Year
DOI
Venue
2021
10.1109/TMI.2021.3078828
IEEE Transactions on Medical Imaging
Keywords
DocType
Volume
Image Processing, Computer-Assisted,Lung
Journal
40
Issue
ISSN
Citations 
9
0278-0062
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Hao Zheng116833.81
Yulei Qin222.87
Yun Gu3317.35
Fangfang Xie400.68
Jie Yang586887.15
Jiayuan Sun600.34
Guang-Zhong Yang72812297.66