Parameter Learning for CRF-Based Tissue Segmentation of Brain Tumors

Meier, Raphael; Karamitsou, Venetia; Habegger, Simon; Wiest, Roland; Reyes, Mauricio (2016). Parameter Learning for CRF-Based Tissue Segmentation of Brain Tumors. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Lecture Notes in Computer Science: Vol. 9556 (pp. 156-167). Cham: Springer International Publishing 10.1007/978-3-319-30858-6_14

Text 978-3-319-30858-6_14.pdf - Published Version Restricted to registered users only Available under License Publisher holds Copyright. Download (407kB)

In this work, we investigated the potential of a recently proposed parameter learning algorithm for Conditional Random Fields (CRFs). Parameters of a pairwise CRF are estimated via a stochastic subgradient descent of a max-margin learning problem. We compared the performance of our brain tumor segmentation method using parameter learning to a version using hand-tuned parameters. Preliminary results on a subset of the BRATS2015 training set show that parameter learning leads to comparable or even improved performance. In addition, we also performed experiments to study the impact of the composition of training data on the final segmentation performance. We found that models trained on mixed data sets achieve reasonable performance compared to models trained on stratified data.

Interest & Impact

Downloads

1 since deposited on 24 May 2017
0 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item