Ms. Tanya L. Niemeyer Profile

Tanya L. Niemeyer

at Confirma Inc

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 | 12 May 2004 Paper

Classification of cardiac-related artifacts in dynamic contrast breast MRI

Keith Stegbauer, Justin Smith, Tanya Niemeyer, Chris Wood

Proceedings Volume 5370, (2004) https://doi.org/10.1117/12.535402

KEYWORDS: Breast, Chest, Magnetic resonance imaging, Heart, Breast cancer, Tissues, Diagnostics, Arteries, Image segmentation, Computer aided design

Read Abstract +

Dynamic contrast breast MRI is becoming an important adjunct in screening women at high risk for breast cancer, determining extent of disease (staging) and monitoring response to therapy. In dynamic contrast breast MRI, regions of rapid contrast uptake indicate increases in vascularity which can be associated with abnormal tissue, sometimes significant for malignant disease. To show these areas of enhancement, subtractions between the pre and post contrast images and maximum intensity projections (MIPs) are computed. Many projections are obscured by normally enhancing anatomy (heart, aorta, pulmonary vessels). Identification of these structures allows their removal from MIPs, which improves image quality, diagnostic utility and the conspicuity of the enhancing regions. In this study, a fully automated classifier is presented which uses the spatial location of enhancing regions to separate those that occur inside the chest wall from those occurring in the tissue of interest (breast, axilla, chest wall). The classifier was trained on 21 studies each acquired at a different institution (699 clusters of pixels), and tested on 7 studies (231 clusters of pixels) that were not part of the training set. Multiple cost functions for training were examined. The measurements for the peak performance of the classifier were sensitivity 97.0%, specificity 99.4%, PPV 99.9%, NPV 78.8%.

Proceedings Article | 12 May 2004 Paper

Comparison of automatic time curve selection methods for breast MR CAD

Tanya Niemeyer, Chris Wood, Keith Stegbauer, Justin Smith

Proceedings Volume 5370, (2004) https://doi.org/10.1117/12.535992

KEYWORDS: Breast, Tissues, Computer aided diagnosis and therapy, Diagnostics, Computer aided design, Magnetic resonance imaging, Breast cancer, Mammography, Biopsy, Tumors

Read Abstract +

Breast MR is being employed to detect, diagnose, and stage breast cancer. With a breast MR study, areas that exhibit rapid uptake of contrast followed by washout behavior have been shown to be indicative of malignant tissue. The most common way to display this temporal information is with a time versus percent enhancement curve that plots the enhancement of the tissue for each series relative to a baseline or pre contrast series. The generation of time curves is commonly done using manual methods, but could easily be automated by a computer to reduce user variability. The information obtained by the time curve can then be used for computer assisted analysis of suspicious areas. We compare two methods for the automated detection of such time curves for 42 malignant lesions. The first method is a previously published technique which finds the highest enhancing 3x3 area of a lesion to generate a curve. The second method is a new hierarchical search that examines end time point behavior in combination with enhancement to find an optimal curve location. The two methods for curve generation are examined in their ability to produce a washout type curve, which has a greater likelihood of being malignant than curves that continue to enhance. The time curves found using highest percent enhancement method showed washout in 52 percent (22/42) of lesions. Using the hierarchical search algorithm, 90 percent (38/42) of lesions showed washout type curves

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