Prof. Ming-Chang Chiang Profile

Prof. Ming-Chang Chiang

at National Yang Ming Chiao Tung Univ

SPIE Involvement:

Author

Publications (2)

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

Automatic infarct segmentation in rat brain MR images after stroke using an adaptive deformable model

Herng-Hua Chang, Shin-Joe Yeh, Yi-Ru Lin, Ming-Chang Chiang, Sung-Tsang Hsieh

Proceedings Volume 12468, 124681E (2023) https://doi.org/10.1117/12.2654532

KEYWORDS: Image segmentation, Magnetic resonance imaging, Brain, Neuroimaging, Diffusion weighted imaging, Deformation, Contour modeling, Image processing, Ischemic stroke, Image enhancement

Read Abstract +

Experimental ischemic stroke models play an important role in realizing the mechanism of cerebral ischemia and evaluating the development of pathological extent. An efficient and reliable image segmentation tool to automatically identify the infarct region in the diffusion weighted imaging (DWI) and T2-weighted MRI (T2WI) images is critical for subsequent processing applications. This paper develops an automatic infarct segmentation algorithm in both rat brain DWI and T2WI images after stroke for further evaluation of neurological damages. The proposed framework consists of four major steps including image preprocessing, image registration, image enhancement, and infarct segmentation. To achieve complete automation, the input rat brain is first divided into two hemispheres, from which the initial infarct mask is acquired after a series of image registration, image subtraction, and image enhancement processes. Subsequently, an adaptive deformable model is exploited to perform infarct region segmentation. The proposed deformable model employs two-phase level set evolution, which is regularized by a local region integration. The integration of the difference between the local intensities and the global mean intensity is restricted in the inward and outward normal directions to minimize the influence of the intensity inhomogeneity. Moreover, the time step is dynamically modified towards annealing for performance refinement. Massive MR images were utilized to evaluate this new infarct segmentation algorithm. Adequate infarct segmentation results were obtained, which outperformed other competitive methods both qualitatively and quantitatively. Our infarct segmentation framework is of potential in providing a decent tool to facilitate preclinical stroke investigation and relevant neuroscience research using DWI and T2WI images.

Proceedings Article | 28 February 2020 Paper

Infarct region segmentation in rat brain T2 MR images after stroke based on fully convolutional networks

Herng-Hua Chang, Shin-Joe Yeh, Ming-Chang Chiang, Sung-Tsang Hsieh

Proceedings Volume 11317, 113172G (2020) https://doi.org/10.1117/12.2548561

KEYWORDS: Image segmentation, Brain, Magnetic resonance imaging, Skull, Neuroimaging, Data modeling, Medical imaging, Ischemia, Algorithm development, Image processing

Read Abstract +

Stroke remains one of the most life-threatening diseases around the world. Rodent stroke models have been widely adopted in experimental ischemia studies for decades. Magnetic resonance imaging (MRI) has been shown effective to reveal the stroke region and associated tissues in many animal studies. Extraction of the infarct regions in rat brain MR images after stroke is crucial for further investigation such as neuro damage analysis and behavior examination. This paper is in an attempt to develop a computer-aided infarct segmentation algorithm based on a fully convolutional network (FCN) for rat stroke model analyses in MR images. In our approach, the entire procedure is divided into two major phases: skull stripping and infarct segmentation. The purpose of the skull stripping process is to provide a clean brain region, from which the infract segmentation is executed. The same FCN is applied to both phases but with different training images and segmentation purposes. Our FCN model consists of 33 convolutional layers, 5 maximum pooling layers, and 5 upsampling layers. The residual network is introduced to the FCN architecture for updating the weights and the batch normalization strategy is exploited to reduce the gradient vanishing problem. To evaluate the proposed FCN framework, 35 subjects of T2-weighted MR images of the rat brain acquired from National Taiwan University, Taipei, Taiwan were utilized. Preliminary experimental results indicated that our method produced high segmentation accuracy regarding skull stripping (Dice = 98.12) and infarct segmentation (Dice = 80.47) across a number of rat brain MR image volumes.

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