Detection and segmentation of pulmonary nodules based on improved 3D VNet algorithm

Fang Liu; Zhencheng Chen; Peng Sun

doi:10.1117/12.2636396

6 May 2022 Detection and segmentation of pulmonary nodules based on improved 3D VNet algorithm

Fang Liu, Zhencheng Chen, Peng Sun

Proceedings Volume 12176, International Conference on Algorithms, Microchips and Network Applications; 121760A (2022) https://doi.org/10.1117/12.2636396
Event: International Conference on Algorithms, Microchips, and Network Applications 2022, 2022, Zhuhai, China

Abstract

Lung cancer has one of the highest incidence rate and mortality rates in the world. Therefore, the recognition and diagnosis of pulmonary nodules based on CT images is an essential index for the diagnosis of lung cancer. Therefore, the detection and segmentation of pulmonary nodules are of great significance in assisting doctors in detecting lung diseases. In this paper, the luna16 data set in the LIDC-IDRI (the lung image database consortium) initiated by the National Cancer Institute is used for research. Given a large amount of data and the diversity of types and sizes of pulmonary nodules in this data set, the detection, and segmentation of various pulmonary nodules are realized based on the improved deep neural network 3D VNet. The deeper the network, the stronger the expression ability and the better the performance. However, with the increase of network depth, there will be problems such as gradient dissipation and gradient explosion. The improved 3D VNet network improves this problem. More than 200000 lung CT images in the LUNA16 data set are preprocessed, such as image denoising and interpolation sampling, and then coarse segmentation images and mask images are generated. They are trained many times in the improved 3D VNet model, and the results with high detection accuracy are obtained. The experimental results show that the dice similarity coefficient (DSC) and IOU of the improved 3D Vnet network can reach 88.29% and 87.25%, which has a good effect on the current detection methods of pulmonary nodules.

Citation Download Citation

Fang Liu, Zhencheng Chen, and Peng Sun "Detection and segmentation of pulmonary nodules based on improved 3D VNet algorithm", Proc. SPIE 12176, International Conference on Algorithms, Microchips and Network Applications, 121760A (6 May 2022); https://doi.org/10.1117/12.2636396

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image segmentation

Lung

Computed tomography

3D modeling

Convolution

Image processing

3D image processing

Show All Keywords

Keywords/Phrases

Search In:

Publication Years