Patient-Specific Semi-Supervised Learning for Postoperative Brain Tumor Segmentation

Meier, Raphael; Bauer, Stefan; Slotboom, Johannes; Wiest, Roland; Reyes, Mauricio (September 2014). Patient-Specific Semi-Supervised Learning for Postoperative Brain Tumor Segmentation. In: Medical Image Computing and Computer-Assisted Intervention — MICCAI 2014. Proceedings, Part I. Lecture Notes in Computer Science: Vol. 17 (pp. 714-721). Springer

[img] Text
MeierMiccai2014.pdf - Accepted Version
Restricted to registered users only
Available under License Publisher holds Copyright.

Download (337kB) | Request a copy

In contrast to preoperative brain tumor segmentation, the problem of postoperative brain tumor segmentation has been rarely approached so far. We present a fully-automatic segmentation method using multimodal magnetic resonance image data and patient-specific semi-supervised learning. The idea behind our semi-supervised approach is to effectively fuse information from both pre- and postoperative image data of the same patient to improve segmentation of the postoperative image. We pose image segmentation as a classification problem and solve it by adopting a semi-supervised decision forest. The method is evaluated on a cohort of 10 high-grade glioma patients, with segmentation performance and computation time comparable or superior to a state-of-the-art brain tumor segmentation method. Moreover, our results confirm that the inclusion of preoperative MR images lead to a better performance regarding postoperative brain tumor segmentation.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

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

UniBE Contributor:

Meier, Raphael; Bauer, Stefan; Slotboom, Johannes; Wiest, Roland and Reyes, Mauricio

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health
000 Computer science, knowledge & systems
600 Technology

ISBN:

978-3-319-10404-1

Series:

Lecture Notes in Computer Science

Publisher:

Springer

Language:

English

Submitter:

Mauricio Antonio Reyes Aguirre

Date Deposited:

05 Jan 2015 15:36

Last Modified:

26 Jun 2016 01:56

PubMed ID:

25333182

BORIS DOI:

10.7892/boris.61000

URI:

https://boris.unibe.ch/id/eprint/61000

Actions (login required)

Edit item Edit item
Provide Feedback