Dr. Yunliang Cai Profile

Dr. Yunliang Cai

at Worcester Polytechnic Institute

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 | 15 February 2021 Poster + Presentation + Paper

A vertebral level-wise data augmentation scheme for segmentation via deep learning

Shaoju Wu, Yunliang Cai, Xiaoyao Fan, Sohail Mirza, Keith Paulsen, Songbai Ji

Proceedings Volume 11596, 1159639 (2021) https://doi.org/10.1117/12.2580937

KEYWORDS: Image segmentation, Spine, Pathology

Read Abstract +

Accurate vertebral segmentation is important for image-related assessment of spine pathologies. Recent spine segmentation techniques focus on deep learning-based methods. However, they typically require a large training dataset with accurate annotations, which may not be feasible in practice. To mitigate the challenge, here we propose a data augmentation scheme based on a single spine to incorporate biomechanically constrained intervertebral motion (rotation and translation) to generate a large training dataset (N=3000). Vertebrae are then segmented using a point cloud-based deep learning architecture, PointNet++, which appears not to have been applied to spine segmentation before. Spine testing samples (N=90) are first generated by applying our data augmentation technique to three separate spines. Segmentation performances are compared with two baseline data augmentation methods that do not include intervertebral motion. Then, we further evaluate our technique based on 8 spine samples from actual image acquisitions. In both cases, we show that incorporation of vertebral level-wise data augmentation improves segmentation accuracy in terms of mean Dice coefficient (e.g., 0.932 vs. 0.902 for augmented testing samples and 0.940 vs. 0.924 for acquired testing samples). These results suggest that our vertebral level-wise data augmentation is useful to facilitate deep learning-based spine segmentation, which is important especially when it is not feasible to generate accurate annotations for a large number of training samples.

Proceedings Article | 16 March 2020 Paper

Stereovision-updated image guidance in multi-level open spine surgery: short vs. long exposure

Xiaoyao Fan, Maxwell Durtschi, Chen Li, Linton Evans, Songbai Ji, Yunliang Cai, Sohail Mirza, Keith Paulsen

Proceedings Volume 11315, 1131529 (2020) https://doi.org/10.1117/12.2549646

KEYWORDS: Image registration, Image processing, Spine, Surgery, Computed tomography, Data acquisition, Navigation systems

Read Abstract +

In open spine surgery, the accuracy of image guidance is compromised by alignment change between supine preoperative CT images (pCT) and prone intraoperative positioning. We have developed a level-wise registration framework to compensate for the intervertebral motion by updating pCT to match with intraoperative stereovision (iSV) data of the exposed spine. In this study, we compared performance of the iSV image updating system in different lengths of exposure using retrospective data from one cadaver pig specimen. Specifically, L1 to L6 were exposed and 3 metallic mini-screws were implanted on each level as “ground truth” locations. The spine was positioned supine to acquire pCT, and then positioned prone to acquire iSV using a hand-held iSV device. One image pair of iSV was acquired from each exposed vertebra. Three exposure lengths were evaluated by selecting data from corresponding levels to compare performance: 6 levels, 4 levels, and 3 levels. Accuracy of iSV updating was assessed through point-to-point registration error (ppRE) using mini-screw locations, and the average accuracy was 1.26±0.77 mm, 1.54±0.62 mm, and 1.38±0.44 mm, for the three exposure lengths, respectively. The time cost was ~10-15 min and similar in all three exposure sizes. Results indicate that performance of iSV image updating was similar in different lengths of exposure, and the accuracy was within clinically acceptable range (2 mm).

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