Title

Lung Segmentation in CT Images using a Fully Convolutional Neural Network with Multi-Instance and Conditional Adversary Loss

Abstract

Lung segmentation is usually the first step of lung CT image analysis and plays an important role in lung disease diagnosis. We propose an efficient end-to-end fully convolutional neural network to segment lungs with different diseases in CT images. We introduce a multi-instance loss and a conditional adversary loss to the neural network in order to solve the segmentation problem for more severe pathological conditions. Our method is capable of solving the lung segmentation problem under normal, moderate and severe pathological conditions, which is validated on 3 public benchmark datasets with different diseases.

Meeting Name

IEEE 15th Annual International Symposium on Biomedical Imaging, ISBI 2018 (2018: Apr. 4-7, Washington, DC)

Department(s)

Computer Science

Research Center/Lab(s)

Intelligent Systems Center

Keywords and Phrases

Lung segmentation; CT; Fully convolutional neural network; Multi-instance; Conditional adversarial network

International Standard Book Number (ISBN)

978-1-5386-3636-7

International Standard Serial Number (ISSN)

1945-8452

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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