Mr. Marc Morcos Profile

Marc Morcos

at Johns Hopkins Univ School of Medicine

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 | 8 March 2019 Presentation + Paper

Automatic applicator digitization for MRI-based cervical cancer brachytherapy planning using two surface models

William Hrinivich, Marc Morcos, Akila Viswanathan, Todd McNutt, Junghoon Lee

Proceedings Volume 10951, 1095116 (2019) https://doi.org/10.1117/12.2513003

KEYWORDS: 3D modeling, Magnetic resonance imaging, Cervical cancer, 3D image processing, Image filtering, Image registration, Radiotherapy

Read Abstract +

Modern image-guided cervical cancer brachytherapy involves the insertion of hollow applicators in the uterus and surrounding the cervix to deliver a radioactive source. These applicators are imaged and manually digitized following insertion for treatment planning. We present an algorithm to automatically digitize these applicators using MRI for cervical cancer brachytherapy planning. Applicators were digitized in vivo using T2-weighted MR images (1.5 T) from 21 brachytherapy fractions including 9 patients. The model-to-image registration algorithm was implemented in C++ involving a 2D matched filter to identify the applicator center, and a 3D surface model to identify local position by maximizing the intensity gradient normal to the surface. Surface models were produced using training MR images. Errors in the algorithm results were calculated as the 3D distances of the applicator tip and center from those identified manually. A model based on manufacturer data was also used for applicator digitization to assess algorithm sensitivity to surface model variation. The algorithm correctly identified the applicator in 20 out of 21 images with mean execution time of 2.5 s. Mean±SD error following digitization using the MRI and manufacturer-based surface models was 1.2±0.6 mm and 1.3±0.7 mm for the tandem tip (p = 0.52), and 1.4±0.9 mm and 1.3±0.7 mm for the ring center (p = 0.61). The algorithm requires no manual initialization with consistent results across surface models, showing promise for clinical implementation.

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