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Philipp Kickingereder

Researcher at University Hospital Heidelberg

Publications -  88
Citations -  7508

Philipp Kickingereder is an academic researcher from University Hospital Heidelberg. The author has contributed to research in topics: Magnetic resonance imaging & Dentate nucleus. The author has an hindex of 36, co-authored 88 publications receiving 5538 citations. Previous affiliations of Philipp Kickingereder include Heidelberg University & University of Cologne.

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Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Spyridon Bakas, +438 more
- 05 Nov 2018 - 
TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.
Journal ArticleDOI

Gadolinium Retention in the Dentate Nucleus and Globus Pallidus Is Dependent on the Class of Contrast Agent

Alexander Radbruch, +9 more
- 06 Apr 2015 - 
TL;DR: This study indicates that an SI increase in the DN and GP on T1-weighted images is caused by serial application of the linear GBCA gadopentetate dimeglumine but not by the macrocyclic GBCAs gadoterate meglumines.
Book ChapterDOI

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

Fabian Isensee, +4 more
TL;DR: In this paper, a convolutional neural network (CNN) was used for brain tumor segmentation and a dice loss function was used to cope with class imbalances and extensive data augmentation to successfully prevent overfitting.
Book ChapterDOI

No New-Net

Fabian Isensee, +4 more
TL;DR: The effectiveness of a well trained U-Net in the context of the BraTS 2018 challenge is demonstrated given that researchers are currently besting each other with architectural modifications that are intended to improve the segmentation performance.
Journal ArticleDOI

Radiomic profiling of glioblastoma: Identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models

Philipp Kickingereder, +10 more
- 20 Jun 2016 - 
TL;DR: An 11-feature radiomic signature that allows prediction of survival and stratification of patients with newly diagnosed glioblastoma was identified, and improved performance compared with that of established clinical and radiologic risk models was demonstrated.