Affiliation:
1. The School of Mathematical Sciences, Zhejiang University, Hangzhou 310027, China
2. The School of Mathematics and Statistics, Xidian University, Xi’an 710069, China
Abstract
Accurate pancreas segmentation from 3D CT volumes is important for pancreas diseases therapy. It is challenging to accurately delineate the pancreas due to the poor intensity contrast and intrinsic large variations in volume, shape, and location. In this paper, we propose a semiautomated deformable U-Net, i.e., DUNet for the pancreas segmentation. The key innovation of our proposed method is a deformable convolution module, which adaptively adds learned offsets to each sampling position of 2D convolutional kernel to enhance feature representation. Combining deformable convolution module with U-Net enables our DUNet to flexibly capture pancreatic features and improve the geometric modeling capability of U-Net. Moreover, a nonlinear Dice-based loss function is designed to tackle the class-imbalanced problem in the pancreas segmentation. Experimental results show that our proposed method outperforms all comparison methods on the same NIH dataset.
Funder
National Natural Science Foundation of China
Subject
Health Informatics,Biomedical Engineering,Surgery,Biotechnology
Cited by
11 articles.
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