Klaus Maier‐Hein
A5027292126- Radiomics and Machine Learning in Medical Imaging
- Advanced Neuroimaging Techniques and Applications
- AI in cancer detection
- Advanced Neural Network Applications
- Advanced MRI Techniques and Applications
- MRI in cancer diagnosis
- COVID-19 diagnosis using AI
- Medical Image Segmentation Techniques
- Artificial Intelligence in Healthcare and Education
- Medical Imaging and Analysis
- Glioma Diagnosis and Treatment
- Brain Tumor Detection and Classification
- Medical Imaging Techniques and Applications
- Generative Adversarial Networks and Image Synthesis
- Functional Brain Connectivity Studies
- Advanced X-ray and CT Imaging
- Optical Imaging and Spectroscopy Techniques
- Surgical Simulation and Training
- Fetal and Pediatric Neurological Disorders
- Lung Cancer Diagnosis and Treatment
- Prostate Cancer Diagnosis and Treatment
- Cell Image Analysis Techniques
- Anomaly Detection Techniques and Applications
- Machine Learning in Healthcare
- Explainable Artificial Intelligence (XAI)
Heidelberg University
2016-2025
National Center for Tumor Diseases
2020-2025
University Hospital Heidelberg
2016-2025
German Cancer Research Center
2016-2025
Deutschen Konsortium für Translationale Krebsforschung
2020-2024
DKFZ-ZMBH Alliance
2015-2024
German Center for Lung Research
2016-2024
Johns Hopkins University
2020-2024
Fred Hutch Cancer Center
2024
Resonance Research (United States)
2024
The image biomarker standardisation initiative (IBSI) is an independent international collaboration which works towards standardising the extraction of biomarkers from acquired imaging for purpose high-throughput quantitative analysis (radiomics). Lack reproducibility and validation studies considered to be a major challenge field. Part this lies in scantiness consensus-based guidelines definitions process translating into biomarkers. IBSI therefore seeks provide nomenclature definitions,...
Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation corresponding tasks has thus been subject intense research over past decades. In this paper, we introduce "Automatic Cardiac Diagnosis Challenge" dataset (ACDC), largest publicly available fully annotated for purpose MRI (CMR) assessment. contains data 150 multi-equipments CMRI recordings...
Abstract Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, approach has not been systematically validated in ground truth studies. Based a simulated data set with tracts, we organized an open international tractography challenge, which resulted 96 distinct submissions from 20 research groups. Here, report encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% bundles (to at least some...
In this work, we report the set-up and results of Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with IEEE International Symposium on Biomedical Imaging (ISBI) 2017 Conferences Medical Image Computing Computer-Assisted Intervention (MICCAI) 2018. The image dataset is diverse contains primary secondary tumors varied sizes appearances various lesion-to-background levels (hyper-/hypo-dense), created collaboration seven hospitals research institutions. Seventy-five...
International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far most widely investigated medical processing task, but various segmentation typically been organized in isolation, such that algorithm development was driven by need to tackle single clinical problem. We hypothesized method capable performing well on multiple tasks will generalize previously unseen task and potentially outperform...
The individual course of white matter fiber tracts is an important key for analysis characteristics in healthy and diseased brains. Uniquely, diffusion-weighted MRI tractography combination with region-based or clustering-based selection streamlines allows the in-vivo delineation anatomically well known tracts. This, however, currently requires complex, computationally intensive tedious-to-set-up processing pipelines. TractSeg a novel convolutional neural network-based approach that directly...
Brain extraction is a critical preprocessing step in the analysis of MRI neuroimaging studies and influences accuracy downstream analyses. The majority brain algorithms are, however, optimized for processing healthy brains thus frequently fail presence pathologically altered or when applied to heterogeneous datasets. Here we introduce new, rigorously validated algorithm (termed HD-BET) relying on artificial neural networks that aims overcome these limitations. We demonstrate HD-BET...
Purpose To evaluate whether radiomic feature–based magnetic resonance (MR) imaging signatures allow prediction of survival and stratification patients with newly diagnosed glioblastoma improved accuracy compared that established clinical radiologic risk models. Materials Methods Retrospective evaluation data was approved by the local ethics committee informed consent waived. A total 119 (allocated in a 2:1 ratio to discovery [n = 79] or validation 40] set) were subjected feature extraction...
The U-Net was presented in 2015. With its straight-forward and successful architecture it quickly evolved to a commonly used benchmark medical image segmentation. adaptation of the novel problems, however, comprises several degrees freedom regarding exact architecture, preprocessing, training inference. These choices are not independent each other substantially impact overall performance. present paper introduces nnU-Net ('no-new-Net'), which refers robust self-adapting framework on basis 2D...
International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical common practices related to organization has not yet been performed. In this paper, we present comprehensive conducted up now. We demonstrate importance and show lack quality control consequences. First, reproducibility interpretation results often hampered as only fraction relevant information typically provided. Second, rank...
Background Men suspected of having clinically significant prostate cancer (sPC) increasingly undergo MRI. The potential deep learning to provide diagnostic support for human interpretation requires further evaluation. Purpose To compare the performance clinical assessment a system optimized segmentation trained with T2-weighted and diffusion MRI in task detection lesions suspicious sPC. Materials Methods In this retrospective study, sequences from consecutive men examined single 3.0-T...
In this work, we report the set-up and results of Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with IEEE International Symposium on Biomedical Imaging (ISBI) 2017 Conferences Medical Image Computing Computer-Assisted Intervention (MICCAI) 2018. The image dataset is diverse contains primary secondary tumors varied sizes appearances various lesion-to-background levels (hyper-/hypo-dense), created collaboration seven hospitals research institutions. Seventy-five...