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

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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.

Item Type:

Book Section (Book Chapter)

Division/Institute:

04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology
04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Meier, Raphael; Habegger, Simon; Wiest, Roland and Reyes, Mauricio

Subjects:

600 Technology > 610 Medicine & health
500 Science > 570 Life sciences; biology
600 Technology > 620 Engineering

ISBN:

978-3-319-30857-9

Series:

Lecture Notes in Computer Science

Publisher:

Springer International Publishing

Language:

English

Submitter:

Raphael Meier

Date Deposited:

24 May 2017 16:32

Last Modified:

24 May 2017 16:32

Publisher DOI:

10.1007/978-3-319-30858-6_14

URI:

https://boris.unibe.ch/id/eprint/97064

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