TY  - JOUR
AU  - Monti, Serena
AU  - Cocozza, Sirio
AU  - Borrelli, Pasquale
AU  - Straub, Sina
AU  - Ladd, Mark
AU  - Salvatore, Marco
AU  - Tedeschi, Enrico
AU  - Palma, Giuseppe
TI  - MAVEN: An Algorithm for Multi-Parametric Automated Segmentation of Brain Veins From Gradient Echo Acquisitions.
JO  - IEEE transactions on medical imaging
VL  - 36
IS  - 5
SN  - 1558-254X
CY  - New York, NY
PB  - Institute of Electrical and Electronics Engineers,
M1  - DKFZ-2017-01258
SP  - 1054 - 1065
PY  - 2017
AB  - Cerebral vein analysis provides a chance to study, from an unusual viewpoint, an entire class of brain diseases, including neurodegenerative disorders and traumatic brain injuries. Manual segmentation approaches can be used to assess vascular anatomy, but they are observer-dependent and time-consuming; therefore, automated approaches are desirable, as they also improve reproducibility. In this paper, a new, fully automated algorithm, based on structural, morphological, and relaxometric information, is proposed to segment the entire cerebral venous system from MR images. The algorithm for multi-parametric automated segmentation of brain VEiNs (MAVEN) is based on a combined investigation of multi-parametric information that allows for rejection of false positives and detection of thin vessels. The method is tested on gradient echo brain data sets acquired at 1.5, 3, and 7 T. It is compared to previous methods against manual segmentation, and its inter-scan reproducibility is assessed. The achieved accuracy and reproducibility are good, meaning that MAVEN outperforms previous methods on both quantitative and qualitative analyses. It is usable at all the field strengths explored, showing comparable accuracy scores, with no need for algorithm parameter adjustments, and thus, it is a promising candidate for the characterization of the venous tree topology.
LB  - PUB:(DE-HGF)16
C6  - pmid:28237923
DO  - DOI:10.1109/TMI.2016.2645286
UR  - https://inrepo02.dkfz.de/record/124379
ER  -