Fully convolutional network with sparse feature-maps composition for automatic lung tumor segmentation from PET images

Haihong Tian; Dehui Xiang; Weifang Zhu; Fei Shi; Xinjian Chen

doi:10.1117/12.2548670

10 March 2020 Fully convolutional network with sparse feature-maps composition for automatic lung tumor segmentation from PET images

Haihong Tian, Dehui Xiang, Weifang Zhu, Fei Shi, Xinjian Chen

Author Affiliations +

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 113131O (2020) https://doi.org/10.1117/12.2548670
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Accurate lung tumor delineation plays an important role in radiotherapy treatment planning. Since the lung tumor has poor boundary in positron emission tomography (PET) images, it is a challenging task to accurately segment lung tumor. In addition, the heart, liver, bones and other tissues generally have the similar gray value as the lung tumor, therefore the segmentation results usually have high false positive. In this paper, we propose a novel and efficient fully convolutional network with a trainable compressed sensing module and deep supervision mechanism with sparse constraints to comprehensively address these challenges; and we call it fully convolutional network with sparse feature-maps composition (SFC-FCN). Our SFC-FCN is able to conduct end-to-end learning and inference, compress redundant features within channels and extract key uncorrelated features. In addition, we use deep a supervision mechanism with sparse constraints to guide the features extraction by a compressed sensing module. The mechanism is developed by driving an objective function that directly guides the training of both lower and upper layers in the network. We have achieved more accurate segmentation results than that of state-of-the-art approaches with a much faster speed and much fewer parameters.

Conference Presentation

Citation Download Citation

Haihong Tian, Dehui Xiang, Weifang Zhu, Fei Shi, and Xinjian Chen "Fully convolutional network with sparse feature-maps composition for automatic lung tumor segmentation from PET images", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113131O (10 March 2020); https://doi.org/10.1117/12.2548670

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available