Prof. Bin Cai Profile

Prof. Bin Cai

at Institute of Intelligent Machines, CAS

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 4 April 2022 Presentation + Paper

Efficient network with ghost tied block for heart segmentation

Yuhao Guo, Bin Cai, Pengpeng Liang, Kaifeng Wang, Zhiyong Sun, Chi Xiong, Bo Song, Chaoshi Niu, Erkang Cheng

Proceedings Volume 12032, 120320A (2022) https://doi.org/10.1117/12.2605538

KEYWORDS: Convolution, Image segmentation, 3D modeling, Heart, Convolutional neural networks

Read Abstract +

Heart segmentation plays an important role in accurate diagnosis and treatment of cardiovascular disease. More recently, deep convolutional neural networks (CNN) are predominant to many medical image analysis applications including 3D heart segmentation. For example, 3D UNet with U-shape encoder-decoder architecture performs well in volumetric segmentation. However, standard convolution, the building block of 3D CNN network usually contains a large number of parameters. In this paper, our objective is to investigate a light convolution module to build an efficient network. Inspired by 2D Tied Block Convolution (TBC), we introduce a Tied Block 3D Convolution (TBC-3D) operator which reuses a small number of convolution filters across each channel group. To this end, TBC-3D requires fewer parameters and is able to obtain more feature maps with high performance. Furthermore, we combine TBC-3D with the Ghost-3D module to construct Ghost Tied Block (GTB). Specifically, Ghost module employs standard convolution (OP1) with few filters to obtain intrinsic feature maps, and then generates more features by cheap linear operation like depth-wise convolution (OP2). TBC-3D is applied in both OP1 and OP2 in the Ghost-3D module. Compared to state-of-the-art solutions using 3D UNet-like architecture, our model with GTB achieves competitive performance on the MM-WHS whole heart segmentation Challenge 2017 datasets with 2.31x less parameters and 1.93x fewer FLOPs.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years