Mr. Hao Zhou Profile

Hao Zhou

at Southern Medical Univ

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 | 7 April 2023 Poster + Paper

Deep learning-based all-in-one combined visualization strategy for disease screening in CT imaging

Hao Zhou, Jingyi Liao, Zhixiong Zeng, Danyang Li, Yongbo Wang, Zhaoying Bian, Dong Zeng, Jianhua Ma

Proceedings Volume 12463, 124633B (2023) https://doi.org/10.1117/12.2654188

KEYWORDS: Computed tomography, Windows, Tunable filters, Lung, Image filtering, Chest, Image sharpness, Visualization, Deep learning, Diseases and disorders

Read Abstract +

Computed tomography (CT) is a widely used medical imaging modality which is capable of displaying the fine details of human body. In clinics, the CT images need to highlight different desired details or structures with different filter kernels and different display windows. To achieve this goal, in this work, we proposed a deep learning based ”All-in-One” (DAIO) combined visualization strategy for high-performance disease screening in the disease screening task. Specifically, the presented DAIO method takes into consideration of both kernel conversion and display window mapping in the deep learning network. First, the sharp kernel, smooth kernel reconstructed images and lung mask are collected for network training. Then, the structure is adaptively transferred to the kernel style through local kernel conversion to make the image have higher diagnostic value. Finally, the dynamic range of the image is compressed to a limited gray level by the mapping operator based on the traditional window settings. Moreover, to promote the structure details enhancement, we introduce a weighted mean filtering loss function. In the experiment, nine of the ten full dose patients cases from the Mayo clinic dataset are utilized to train the presented DAIO method, and one patient case from the Mayo clinic dataset are used for test. Results shows that the proposed DAIO method can merge multiple kernels and multiple window settings into a single one for the disease screening.

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