Mr. Jin Yang Profile

Jin Yang

at Washington Univ in St. Louis

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 | 10 April 2023 Poster + Paper

Abdominal CT pancreas segmentation using multi-scale convolution with aggregated transformations

Jin Yang, Daniel Marcus, Aristeidis Sotiras

Proceedings Volume 12465, 124651R (2023) https://doi.org/10.1117/12.2651702

KEYWORDS: Convolution, Image segmentation, Pancreas, Computed tomography, Medical imaging, Feature extraction, Multiscale representation, Deep learning, Convolutional neural networks

Read Abstract +

Convolutional neural networks (CNNs) are a popular choice for medical image segmentation. However, they may be challenged by the large inter-subject variation in organ shapes and sizes due to CNNs typically employing convolutions with fixed-sized local receptive fields. To address this limitation, we proposed multi-scale aggregated residual convolution (MARC) and iterative multi-scale aggregated residual convolution (iMARC) to capture finer and richer features at various scales. Our goal is to improve single convolutions’ representation capabilities. This is achieved by employing convolutions with varying-sized receptive fields, combining multiple convolutions into a deeper one, and dividing single convolutions into a set of channel-independent sub-convolutions. These implementations result in an increase in their depth, width, and cardinality. The proposed MARC and iMARC can be easily integrated into general CNN architectures and trained end-to-end. To evaluate the improvements of MARC and iMARC on CNNs’ segmentation capabilities, we integrated MARC and iMARC into a standard 2D U-Net architecture for pancreas segmentation on abdominal computed tomography (CT) images. The results showed that our proposed MARC and iMARC enhanced the representation capabilities of single convolutions, resulting in improved segmentation performance with lower computational complexity.

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