Mr. Enze A. Zhou Profile

Enze A. Zhou

at Huazhong Univ. of Science and Technology

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 | 20 November 2019 Paper

DDeep3M-based neuronal cell counting in 2D large-scale images

Xianghan Kong, Shuai Yan, Enze Zhou, Jin Huang, Xinglong Wu, Ping Wang, Shangbin Chen

Proceedings Volume 11190, 1119037 (2019) https://doi.org/10.1117/12.2537797

KEYWORDS: Image segmentation, Brain, Neurons, Brain mapping, Brain imaging, Convolutional neural networks, Raman scattering, Microscopy, Neuroimaging, Target acquisition

Read Abstract +

The number of neuronal cells is fundamentally important for brain functions. However, it can be difficult to obtain the accurate number of neuronal cells in large-scale brain imaging, which is nearly inevitable with traditional image segmentation techniques due to the low contrast and noisy background. Here, we introduce a Docker-based deep convolutional neural network (DDeep3M) for better counting neurons in the stimulated Raman scattering (SRS) microscopy images. To reconcile the memory limit of computational resource, a high-resolution 2D SRS image of whole coronal slice of mouse brain is divided into multiple patch images. Each patch image is then fed into the DDeep3M and predicted as a probability map. A higher contrast image targeting neurons (i.e. the predicted image) can be acquired by stitching the patches of probability map together. With this routine segmentation method applied in both raw SRS image and the predicted image, the DDeep3M achieves the accuracy of over 0.96 for cell counting which is much better than the result of traditional segmentation methods. Compared with the U-Net, which is one of the most popular deep learning networks for medical image segmentation, DDeep3M demonstrates a better result when handling such large-scale image. Thus, DDeep3M can be really helpful for large-scale cell counting in brain research.

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