By Topic

Lymph node segmentation in CT images using a size invariant Mass Spring Model

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Sebastian Steger ; Department of Cognitive Computing & Medical Imaging, Fraunhofer IGD, 64283 Darmstadt, Germany ; Marius Erdt

One major challenge in automated segmentation of lymph nodes in CT scans is the high variance in terms of texture, surrounding tissue, shape and also size. Mass Spring Models have been proven to be suitable for this task. However due to their size preserving property, their performance is highly affected by the size of the target structures. This paper addresses this point by introducing a size invariant Mass Spring Model, which relates to relative rest lengths, has balanced torsion forces and an initial model expansion. We evaluated our method on a set of 25 lymph nodes from routinely gathered CT images and compared it to state of the art Mass Spring Models with different initial sizes. The average Dice Similarity Coefficient toward gold standard was 0.72 for our method compared to 0.61 for the best fitted state of the art model. Thus our method can be successfully applied to clinical relevant lymph nodes of different size without prior knowledge about the size of the target structures in contrast to existing methods.

Published in:

Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine

Date of Conference:

3-5 Nov. 2010