Prof. Xinzui Wang Profile

Prof. Xinzui Wang

at Ji Hua Lab

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 | 3 October 2024 Paper

Fundus image segmentation network based on skipping connection and multiattention fusion

Jikun Su, Xinzui Wang, Shichen Su, Fucheng Cao

Proceedings Volume 13272, 132721C (2024) https://doi.org/10.1117/12.3048058

KEYWORDS: Image segmentation, Data modeling, Image processing, Image enhancement, Retinal diseases, Process modeling, Machine learning, Network architectures, Performance modeling, In vivo imaging

Read Abstract +

The segmentation of retinal vessels is of paramount importance for the diagnosis of diseases and the performance of surgical procedures. With the advent de novo of in silico technologies, the field of in vivo vascular segmentation has witnessed a veritable explosion of innovation. In traditional segmentation networks, the challenge lies in the unclear delineation of vessels, which frequently results in a loss of important data. In response to this quandary, we present an innovative technique for vessel segmentation as part of our investigative work—the Lightweight Fully Connected MultiScale Attention Network (SCB-UNet). The use of data augmentation allows for the acquisition of a substantial quantity of annotated data without the necessity of utilizing thousands of datasets. This network combines the Full-Resolution UNet (FR-UNET) and the Full-Scale Cooperative Network (UNET3+) while integrating spatial and channel attention modules (CBAM). The network expands through high-resolution convolution interactions while preserving image resolution. Within the network, neighboring features are fused, and features from previous images are incorporated to gather contextual information. To enhance feature maps, attention fusion modules are introduced, in order to enhance the expressiveness of feature maps, it is necessary to concurrently strengthen them across spatial as well as channel perspectives. The efficacy in vivo of a novel network was evaluated on retinal vessel datasets (DRIVE, CHAIN_DB_1, & STARE). The results demonstrate in vivo efficacy superior to that of the aforementioned methods, thereby showcasing the network’s status as a highly efficacious and state-of-the-art approach.

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