activity
20182021
most citedAutomated brain extraction of multi-sequence MRI using artificial neural networks

762 citations · 767 across the 2 of their papers we have counts for

collaborators

5 papers

eess.IV2021

Continuous-Time Deep Glioma Growth Models

Jens Petersen, Fabian Isensee, Gregor Köhler +9

The ability to estimate how a tumor might evolve in the future could have tremendous clinical benefits, from improved treatment decisions to better dose distribution in radiation t…

eess.IV2019

Deep Probabilistic Modeling of Glioma Growth

Jens Petersen, Paul F. Jäger, Fabian Isensee +8

Existing approaches to modeling the dynamics of brain tumor growth, specifically glioma, employ biologically inspired models of cell diffusion, using image data to estimate the ass…

cs.CV2019★ 762 cited

Automated brain extraction of multi-sequence MRI using artificial neural networks

Fabian Isensee, Marianne Schell, Irada Tursunova +10

Brain extraction is a critical preprocessing step in the analysis of MRI neuroimaging studies and influences the accuracy of downstream analyses. The majority of brain extraction a…

cs.CV2018★ 5 cited

No New-Net

Fabian Isensee, Philipp Kickingereder, Wolfgang Wick +2

In this paper we demonstrate the effectiveness of a well trained U-Net in the context of the BraTS 2018 challenge. This endeavour is particularly interesting given that researchers…

cs.CV2018

Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge

Fabian Isensee, Philipp Kickingereder, Wolfgang Wick +2

Quantitative analysis of brain tumors is critical for clinical decision making. While manual segmentation is tedious, time consuming and subjective, this task is at the same time v…