A new ASM framework for left ventricle segmentation exploring slice variability in cardiac MRI volumes

A new ASM framework for left ventricle segmentation exploring slice variability in cardiac MRI... Three-dimensional active shape models use a set of annotated volumes to learn a shape model. Using unique landmarks to define the surface models in the training set, the shape model is able to learn the expected shape and variation modes of the segmentation. This information is then used during the segmentation process to impose shape constraints. A relevant problem in which these models are used is the segmentation of the left ventricle in 3D MRI volumes. In this problem, the annotations correspond to a set of contours that define the LV border at each volume slice. However, each volume has a different number of slices (thus, a different number of landmarks), which makes model learning difficult. Furthermore, motion artifacts and the large distance between slices make interpolation of voxel intensities a bad choice when applying the learned model to a test volume. These two problems raise the following questions: (1) how can we learn a shape model from volumes with a variable number of slices? and (2) how can we segment a test volume without interpolating voxel intensities between slices? This paper provides an answer to these questions by proposing a framework to deal with the variable number of slices in the training set and a resampling strategy for the test phase to segment the left ventricle in cardiac MRI volumes with any number of slices. The proposed method was evaluated on a public database with 660 volumes of both healthy and diseased patients, with promising results. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neural Computing and Applications Springer Journals

A new ASM framework for left ventricle segmentation exploring slice variability in cardiac MRI volumes

Loading next page...
Springer London
Copyright © 2016 by The Natural Computing Applications Forum
Computer Science; Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery; Probability and Statistics in Computer Science; Computational Science and Engineering; Image Processing and Computer Vision; Computational Biology/Bioinformatics
Publisher site
See Article on Publisher Site


You’re reading a free preview. Subscribe to read the entire article.

DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

Monthly Plan

  • Read unlimited articles
  • Personalized recommendations
  • No expiration
  • Print 20 pages per month
  • 20% off on PDF purchases
  • Organize your research
  • Get updates on your journals and topic searches


Start Free Trial

14-day Free Trial

Best Deal — 39% off

Annual Plan

  • All the features of the Professional Plan, but for 39% off!
  • Billed annually
  • No expiration
  • For the normal price of 10 articles elsewhere, you get one full year of unlimited access to articles.



billed annually
Start Free Trial

14-day Free Trial