Maier-Hein, Klaus H.

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

Zwanenburg, A., Vallières, M., Abdalah, M. A., Aerts, H. J. W. L., Andrearczyk, V., Apte, A., Ashrafinia, S., Bakas, S., Beukinga, R. J., Boellaard, R., et al. (2020) · Radiology

Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification Techniques

Cited by 4,177

Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?

Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., Yang, X., Heng, P.-A., Cetin, I., Lekadir, K., Camara, O., Gonzalez Ballester, M. A., et al. (2018) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesCardiac Imaging and Diagnostics

Cited by 2,431

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

Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R. T., Berger, C., Ha, S. M., Rozycki, M., et al. (2018) · Apollo - University of Cambridge Repository

Brain Tumor Detection and ClassificationMedical Image Segmentation Techniques

Cited by 1,441

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge

Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R. T., Berger, C., Ha, S. M., Rozycki, M., et al. (2018) · arXiv

Brain Tumor Detection and ClassificationMedical Image Segmentation Techniques

Cited by 1,441

The Liver Tumor Segmentation Benchmark (LiTS)

Bilic, P., Christ, P., Li, H. B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G. E. H., Chartrand, G., et al. (2019) · arXiv

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 1,412

The Liver Tumor Segmentation Benchmark (LiTS)

Bilic, P., Christ, P., Li, H. B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G. E. H., Chartrand, G., et al. (2023) · Medical Image Analysis

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 1,412

The Medical Segmentation Decathlon

Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B. A., Litjens, G., Menze, B., Ronneberger, O., Summers, R. M., et al. (2022) · Nature Communications

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 1,384

TractSeg - Fast and accurate white matter tract segmentation

Wasserthal, J., Neher, P., & Maier-Hein, K. H. (2018) · NeuroImage

Advanced Neuroimaging Techniques and ApplicationsMedical Image Segmentation Techniques

Cited by 664

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

Kleesiek, J., Urban, G., Hubert, A., Schwarz, D., Maier-Hein, K., Bendszus, M., & Biller, A. (2016) · NeuroImage

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 505

Methodological considerations on tract-based spatial statistics (TBSS)

Bach, M., Laun, F. B., Leemans, A., Tax, C. M. W., Biessels, G. J., Stieltjes, B., & Maier-Hein, K. H. (2014) · NeuroImage

Advanced Neuroimaging Techniques and ApplicationsFunctional Brain Connectivity Studies

Cited by 445

A probabilistic u-net for segmentation of ambiguous images

Kohl, S. A. A., Romera-Paredes, B., Meyer, C., De Fauw, J., Ledsam, J. R., Maier-Hein, K. H., Eslami, S. M. A., Rezende, D. J., & Ronneberger, O. (2018) · arXiv

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 439

Federated learning enables big data for rare cancer boundary detection

Pati, S., Baid, U., Edwards, B., Sheller, M., Wang, S.-H., Reina, G. A., Foley, P., Gruzdev, A., Karkada, D., Davatzikos, C., et al. (2022) · Nature Communications

Privacy-Preserving Technologies in DataBrain Tumor Detection and Classification

Cited by 394

No New-Net

Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., & Maier-Hein, K. H. (2019) · Lecture Notes in Computer Science

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 341

Understanding metric-related pitfalls in image analysis validation

Reinke, A., Tizabi, M. D., Baumgartner, M., Eisenmann, M., Heckmann-Nötzel, D., Kavur, A. E., Rädsch, T., Sudre, C. H., Acion, L., Antonelli, M., et al. (2024) · Nature Methods

Cell Image Analysis TechniquesMedical Image Segmentation Techniques

Cited by 241

Common Limitations of Image Processing Metrics: A Picture Story

Reinke, A., Tizabi, M. D., Sudre, C. H., Eisenmann, M., Rädsch, T., Baumgartner, M., Acion, L., Antonelli, M., Arbel, T., Bakas, S., et al. (2021) · arXiv

Medical Image Segmentation TechniquesCell Image Analysis Techniques

Cited by 126

DALSA: domain adaptation for supervised learning from sparsely annotated MR images

Goetz, M., Weber, C., Binczyk, F., Polanska, J., Tarnawski, R., Bobek-Billewicz, B., Koethe, U., Kleesiek, J., Stieltjes, B., & Maier-Hein, K. H. (2016) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 98

Fiber tractography using machine learning

Neher, P. F., Côté, M.-A., Houde, J.-C., Descoteaux, M., & Maier-Hein, K. H. (2017) · NeuroImage

Advanced Neuroimaging Techniques and ApplicationsFunctional Brain Connectivity Studies

Cited by 68

Metrics reloaded: Recommendations for image analysis validation

Maier-Hein, L., Reinke, A., Godau, P., Tizabi, M. D., Buettner, F., Christodoulou, E., Glocker, B., Isensee, F., Kleesiek, J., Kozubek, M., et al. (2022) · arXiv

Cell Image Analysis TechniquesMedical Image Segmentation Techniques

Cited by 59

A Machine Learning Based Approach to Fiber Tractography Using Classifier Voting

Neher, P. F., Götz, M., Norajitra, T., Weber, C., & Maier-Hein, K. H. (2015) · Lecture Notes in Computer Science

Advanced Neuroimaging Techniques and ApplicationsFunctional Brain Connectivity Studies

Cited by 47

Automated detection and quantification of brain metastases on clinical MRI data using artificial neural networks

Pflüger, I., Wald, T., Isensee, F., Schell, M., Meredig, H., Schlamp, K., Bernhardt, D., Brugnara, G., Heußel, C. P., Debus, J., et al. (2022) · Neuro-Oncology Advances

Brain Tumor Detection and ClassificationBrain Metastases and Treatment

Cited by 31

Fine-tuning BERT Models for Summarizing German Radiology Findings

Liang, S., Kades, K., Fink, M., Full, P., Weber, T., Kleesiek, J., Strube, M., & Maier-Hein, K. (2022) · Proceedings of the 4th Clinical Natural Language Processing Workshop

Topic ModelingMultimodal Machine Learning Applications

Cited by 8

USER-CENTERED DESIGN AND USABILITY EVALUATION OF A CANCER PREVENTION WEB APPLICATION: AN ITERATIVE APPROACH IN GERMANY

Carrera, P., Elias, O., Zhang, R., Norajitra, T., Goncalves, A., & Maier-Hein, K. (2025) · medRxiv

Usability and User Interface DesignMobile Health and mHealth Applications

Cited by 1