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Markus Rempfler

Researcher at Friedrich Miescher Institute for Biomedical Research

Publications -  44
Citations -  3781

Markus Rempfler is an academic researcher from Friedrich Miescher Institute for Biomedical Research. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 15, co-authored 44 publications receiving 2367 citations. Previous affiliations of Markus Rempfler include ETH Zurich & University of Borås.

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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.
Posted Content

The Liver Tumor Segmentation Benchmark (LiTS)

Patrick Bilic, +58 more
- 13 Jan 2019 - 
TL;DR: The set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 and International Conference on Medical Image Computing Computer Assisted Intervention (MICCAI) 2017 are reported.
Book ChapterDOI

Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields

Patrick Ferdinand Christ, +12 more
TL;DR: In this paper, a method to automatically segment liver and lesions in CT abdomen images using cascaded fully convolutional neural networks (CFCNs) and dense 3D conditional random fields (CRFs) is presented.
Journal ArticleDOI

Self-organization and symmetry breaking in intestinal organoid development

Denise Serra, +20 more
- 24 Apr 2019 - 
TL;DR: These findings reveal how single cells exposed to a uniform growth-promoting environment have the intrinsic ability to generate emergent, self-organized behaviour that results in the formation of complex multicellular asymmetric structures.
Posted Content

Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks.

Patrick Ferdinand Christ, +19 more
- 20 Feb 2017 - 
TL;DR: Validations on further datasets show that CFCN-based semantic liver and lesion segmentation achieves Dice scores over 94% for liver with computation times below 100s per volume.