Modified Hybrid Task Cascade for Lung Nodules Segmentation in CT Images with Guided Anchoring
Authors: 
Shihuai Xu, Huijuan Lu, Minchao Ye, Ke Yan, Wenjie Zhu, 
Qun JinAuthors Info & Claims
Pages 433 - 438
Abstract
As lung cancer continues to threaten human health, Computer-Aided Diagnostic (CAD) plays an increasingly significant role in lung cancer diagnosis, and convolutional neural networks (CNNs) have shown the outstanding performance in image segmentation. In this work, Hybrid Task Cascade (HTC) is used to segment lung nodules that are difficult to find in CT images. Considering that lung nodules are usually quite small, this study integrates Feature Pyramid Network (FPN) into ResNet-50 to make full use of multi-scale feature and improve the segmentation accuracy of small target nodules. In addition, given that existing defects in Region Proposal Network (RPN), which refers to most of generated anchors are irrelevant to target objects, and the conventional method are unaware of the shapes of target objects, this work proposes to use Guided Anchoring to replace RPN in HTC and generate anchors more effectively. Experimental results on the LIDC-IDRI dataset demonstrate that the modified HTC improves the segmentation accuracy of lung nodules.
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Index Terms
- Modified Hybrid Task Cascade for Lung Nodules Segmentation in CT Images with Guided Anchoring
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Published In
February 2020
607 pages
ISBN:9781450376426
DOI:10.1145/3383972
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- Shenzhen University: Shenzhen University
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Association for Computing Machinery
New York, NY, United States
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Published: 26 May 2020
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- Refereed limited
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- National Natural Science Foundation of China
- Natural Science Foundation of Zhejiang Province
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ICMLC 2020
ICMLC 2020: 2020 12th International Conference on Machine Learning and Computing
February 15 - 17, 2020
Shenzhen, China
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