Automatic segmentation of lesions for the computer-assisted detection in fluorescence urology

Andreas Kage; Wolfgang Legal; Peter Kelm; Jörg Simon; Tobias Bergen; Christian Münzenmayer; Michaela Benz

doi:10.1117/12.911366

23 February 2012 Automatic segmentation of lesions for the computer-assisted detection in fluorescence urology

Andreas Kage, Wolfgang Legal, Peter Kelm, Jörg Simon, Tobias Bergen, Christian Münzenmayer, Michaela Benz

Proceedings Volume 8315, Medical Imaging 2012: Computer-Aided Diagnosis; 83151O (2012) https://doi.org/10.1117/12.911366
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

Bladder cancer is one of the most common cancers in the western world. The diagnosis in Germany is based on the visual inspection of the bladder. This inspection performed with a cystoscope is a challenging task as some kinds of abnormal tissues do not differ much in their appearance from their surrounding healthy tissue. Fluorescence Cystoscopy has the potential to increase the detection rate. A liquid marker introduced into the bladder in advance of the inspection is concentrated in areas with high metabolism. Thus these areas appear as bright "glowing". Unfortunately, the fluorescence image contains besides the glowing of the suspicious lesions no more further visual information like for example the appearance of the blood vessels. A visual judgment of the lesion as well as a precise treatment has to be done using white light illumination. Thereby, the spatial information of the lesion provided by the fluorescence image has to be guessed by the clinical expert. This leads to a time consuming procedure due to many switches between the modalities and increases the risk of mistreatment. We introduce an automatic approach, which detects and segments any suspicious lesion in the fluorescence image automatically once the image was classified as a fluorescence image. The area of the contour of the detected lesion is transferred to the corresponding white light image and provide the clinical expert the spatial information of the lesion. The advantage of this approach is, that the clinical expert gets the spatial and the visual information of the lesion together in one image. This can save time and decrease the risk of an incomplete removal of a malign lesion.

Citation Download Citation

Andreas Kage, Wolfgang Legal, Peter Kelm, Jörg Simon, Tobias Bergen, Christian Münzenmayer, and Michaela Benz "Automatic segmentation of lesions for the computer-assisted detection in fluorescence urology", Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83151O (23 February 2012); https://doi.org/10.1117/12.911366

ACCESS THE FULL ARTICLE

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: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Luminescence

Tissues

Image segmentation

Databases

Visualization

Information visualization

Bladder

Show All Keywords

Keywords/Phrases

Search In:

Publication Years