Klaus Maier‐Hein

Research interests

Publications

• nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation

  Lecture notes in computer science · 2024 · 10.1007/978-3-031-72114-4_47

• Metrics reloaded: recommendations for image analysis validation

  Nature Methods · 2024 · 10.1038/s41592-023-02151-z

• Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study

  The Lancet Oncology · 2024 · 10.1016/s1470-2045(24)00220-1

• MedNeXt: Transformer-Driven Scaling of ConvNets for Medical Image Segmentation

  Lecture notes in computer science · 2023 · 10.1007/978-3-031-43901-8_39

• The Medical Segmentation Decathlon

  Nature Communications · 2022 · https://doi.org/10.1038/s41467-022-30695-9

• The Liver Tumor Segmentation Benchmark (LiTS)

  Medical Image Analysis · 2022 · https://doi.org/10.1016/j.media.2022.102680

• MONAI: An open-source framework for deep learning in healthcare

  arXiv (Cornell University) · 2022 · 10.48550/arxiv.2211.02701

• Federated learning enables big data for rare cancer boundary detection

  Nature Communications · 2022 · 10.1038/s41467-022-33407-5

• The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

  Radiology · 2020 · https://doi.org/10.1148/radiol.2020191145

• CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation

  Medical Image Analysis · 2020 · 10.1016/j.media.2020.101950

• No New-Net

  Lecture notes in computer science · 2019 · 10.1007/978-3-030-11726-9_21

• Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment

  Radiology · 2019 · 10.1148/radiol.2019190938

• Automated brain extraction of multisequence MRI using artificial neural networks

  Human Brain Mapping · 2019 · 10.1002/hbm.24750

• Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study

  The Lancet Oncology · 2019 · 10.1016/s1470-2045(19)30098-1

• Abstract: nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation

  Informatik aktuell · 2019 · 10.1007/978-3-658-25326-4_7

• TractSeg - Fast and accurate white matter tract segmentation

  NeuroImage · 2018 · 10.1016/j.neuroimage.2018.07.070

• nnU-Net: Self-adapting Framework for U-Net-Based Medical Image\n Segmentation

  arXiv (Cornell University) · 2018 · 10.48550/arxiv.1809.10486

• Why rankings of biomedical image analysis competitions should be interpreted with care

  Nature Communications · 2018 · 10.1038/s41467-018-07619-7

• Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

  IEEE Transactions on Medical Imaging · 2018 · https://doi.org/10.1109/tmi.2018.2837502

• The challenge of mapping the human connectome based on diffusion tractography

  Nature Communications · 2017 · https://doi.org/10.1038/s41467-017-01285-x

• Deep MRI brain extraction: A 3D convolutional neural network for skull stripping

  NeuroImage · 2016 · 10.1016/j.neuroimage.2016.01.024

• ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

  Medical Image Analysis · 2016 · 10.1016/j.media.2016.07.009

• Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models

  Radiology · 2016 · 10.1148/radiol.2016160845

• Methodological considerations on tract-based spatial statistics (TBSS)

  NeuroImage · 2014 · 10.1016/j.neuroimage.2014.06.021

• The Medical Imaging Interaction Toolkit: challenges and advances

  International Journal of Computer Assisted Radiology and Surgery · 2013 · 10.1007/s11548-013-0840-8

Current projects

No projects listed.