Mr. Michael Schmied Profile

Michael Schmied

at Technische Univ Graz

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 | 15 February 2021 Presentation + Paper

A patch-based-approach for aortic landmarking

Michael Schmied, Antonio Pepe, Jan Egger

Proceedings Volume 11600, 1160010 (2021) https://doi.org/10.1117/12.2580718

KEYWORDS: Angiography, Computed tomography, Image classification, Convolutional neural networks, Aorta

Read Abstract +

Accurate landmark localization in 3D medical volumes is nowadays needed for many medical and technical applications such as segmentation, detection of anatomical structures, image registration, and more. Due to the time-consuming process of manual landmarking and the variety of landmark positioning by individual observers, a more uniform and automatic solution is needed. This paper examines a state-of-the-art approach based on a patch-based Convolutional Neural Network (CNN), that is able to predict multiple landmarks simultaneously, and its performance on ill-posed landmarks. It combines regression and classification in one framework to improve the localization accuracy of individual landmarks by taking into consideration the relationship between an image patch and an anatomical landmark position. During inference, image patches are passed to the CNN as input, which are then moved by an amount, specified by the regression output, in the direction of highest probability, which is specified by the classification output. One of the main challenges here was that there are no salient landmarks in the aorta, they are all just generic points in a tube. This adds variability in the manual positioning of the landmarks and even the predictions of the network for each volume. The network’s prediction accuracy was quantitatively evaluated and the best setup achieved an average landmark localization error of 16.04mm, with 99.0% of all predictions having a mean error smaller than 30 mm, while the best prediction during evaluation had a mean error of 3.53 mm over all landmarks.

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