Helbling collaborator wins Young Scientist Impact Award

Publikationen

We proudly announce our collaborator Dr. Stefan Bauer has been honored with the Young Scientist Impact Award in the category Medical Image Analysis by the International Society for Medical Image Computing and Computer Assisted Interventions ( http://miccai.org/miccai-2016-athens-greece ). Stefan’s scientific publication entitled Fully Automatic Segmentation of Brain Tumor Images Using Support Vector Machine Classification in Combination with Hierarchical Conditional Random Field Regularization has been the most cited paper in the MICCAI Journal over the last 5 years:
Abstract Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times. (The publication can be downloaded at the following link: http://link.springer.com/chapter/10.1007%2F978-3-642-23626-6_44 )
MRI image slice of a glioma patient showing automatically segmented healthy and pathologic tissue.