Klaus Hermann Maier-Hein

Klaus Hermann Maier-Hein
German Cancer Research Center | DKFZ · Junior Group Medical Image Computing

PD Dr. rer. nat.

About

409
Publications
85,844
Reads
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12,540
Citations
Additional affiliations
June 2014 - present
German Cancer Research Center
Position
  • Head of Medical Image Computing (junior group)
September 2011 - November 2011
Harvard Medical School
Position
  • PostDoc Position
March 2010 - May 2014
German Cancer Research Center
Position
  • Head of Computational Disease Analysis Group
Description
  • Neuroimaging, Diffusion Imaging, Tumor Imaging

Publications

Publications (409)
Article
Background To assess whether AI-based decision support allows more reproducible and standardized assessment of treatment response on MRI in neuro-oncology as compared to manual 2-dimensional measurements of tumor burden using the RANO criteria. Methods A series of 30 patients (15 lower-grade gliomas, 15 glioblastoma) with availability of consecuti...
Article
Full-text available
International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)—a biomedic...
Article
Full-text available
Fluoroscopy-guided trauma and orthopedic surgeries involve the repeated acquisition of correct anatomy-specific standard projections for guidance, monitoring, and evaluating the surgical result. C-arm positioning is usually performed by hand, involving repeated or even continuous fluoroscopy at a cost of radiation exposure and time. We propose to a...
Article
Full-text available
Visual discrimination of tissue during surgery can be challenging since different tissues appear similar to the human eye. Hyperspectral imaging (HSI) removes this limitation by associating each pixel with high-dimensional spectral information. While previous work has shown its general potential to discriminate tissue, clinical translation has been...
Preprint
Full-text available
The field of automatic biomedical image analysis crucially depends on robust and meaningful performance metrics for algorithm validation. Current metric usage, however, is often ill-informed and does not reflect the underlying domain interest. Here, we present a comprehensive framework that guides researchers towards choosing performance metrics in...
Article
Objectives: Disseminated bone marrow (BM) involvement is frequent in multiple myeloma (MM). Whole-body magnetic resonance imaging (wb-MRI) enables to evaluate the whole BM. Reading of such whole-body scans is time-consuming, and yet radiologists can transfer only a small fraction of the information of the imaging data set to the report. This limit...
Article
Background context : Navigation and robotic systems have been increasingly applied to spinal instrumentation but dedicated screw planning is a time-consuming prerequisite to tap the full potential of these techniques. Purpose : To develop and validate an automated planning tool for lumbosacral pedicle screw placement using a convolutional neural n...
Article
Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust deployment of machine learning algorithms in medicine. When the algorithms encounter cases that deviate from the distribution of the training data, they often produce incorrect and over-confident predictions. OoD detection algorithms aim to catch erroneous...
Preprint
Full-text available
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Fede...
Article
Objectives: The aim of this study was to estimate the prospective utility of a previously retrospectively validated convolutional neural network (CNN) for prostate cancer (PC) detection on prostate magnetic resonance imaging (MRI). Materials and methods: The biparametric (T2-weighted and diffusion-weighted) portion of clinical multiparametric pr...
Chapter
Unsupervised anomaly detection is often attributed great promise, especially for rare conditions and fast adaptation to novel conditions or imaging techniques without the need for explicitly labeled data. However, most previous works study different methods in a constrained research setting with a limited number of common types of pathologies. Here...
Preprint
Visual discrimination of tissue during surgery can be challenging since different tissues appear similar to the human eye. Hyperspectral imaging (HSI) removes this limitation by associating each pixel with high-dimensional spectral information. While previous work has shown its general potential to discriminate tissue, clinical translation has been...
Chapter
Trauma and orthopedic surgeries that involve fluoroscopic guidance crucially depend on the acquisition of correct anatomy-specific standard projections for monitoring and evaluating the surgical result. This implies repeated acquisitions or even continuous fluoroscopy. To reduce radiation exposure and time, we propose to automate this procedure and...
Article
Full-text available
Background/objectives The aim of this study was to compare intravoxel incoherent motion (IVIM) diffusion weighted (DW) MRI and CT perfusion to assess tumor perfusion of pancreatic ductal adenocarcinoma (PDAC). Methods In this prospective study, DW-MRI and CT perfusion were conducted in nineteen patients with PDAC on the day before surgery. IVIM an...
Article
Visual discrimination of tissue during surgery can be challenging since different tissues appear similar to the human eye. Hyperspectral imaging (HSI) removes this limitation by associating each pixel with high-dimensional spectral information. While previous work has shown its general potential to discriminate tissue, clinical translation has been...
Preprint
Full-text available
Visual discrimination of tissue during surgery can be challenging since different tissues appear similar to the human eye. Hyperspectral imaging (HSI) removes this limitation by associating each pixel with high-dimensional spectral information. While previous work has shown its general potential to discriminate tissue, clinical translation has been...
Article
Objective Fiber tractography(FT) has become an important non-invasive tool to ensure maximal safe tumor resection in eloquent glioma surgery. Intraoperatively applied FT is still predominantly based on Diffusion Tensor Imaging(DTI). However, reconstruction schemes of high angular resolution diffusion imaging(HARDI) data for high resolution fiber tr...
Article
Multispectral imaging provides valuable information on tissue composition such as hemoglobin oxygen saturation. However, the real-time application of this technique in interventional medicine can be challenging due to the long acquisition times needed for large amounts of hyperspectral data with hundreds of bands. While this challenge can partially...
Article
Full-text available
Background Gadolinium-based contrast agents (GBCAs) are widely used to enhance tissue contrast during MRI scans and play a crucial role in the management of patients with cancer. However, studies have shown gadolinium deposition in the brain after repeated GBCA administration with yet unknown clinical significance. We aimed to assess the feasibilit...
Article
Background Whole-body magnetic resonance imaging (wb-MRI) is an important diagnostic tool for staging, risk assessment and response evaluation in myeloma. Wb-MRIs contain approximately 110 million voxels per sequence, and only a limited amount of this information can be processed and reported by radiologists to date. Deep learning has brought strik...
Article
Background Advances in deep learning have made automatic biomedical image segmentation feasible. Additionally, radiomics analysis now allows computer based, in-depth tissue analysis from medical images. The goal of this work was to establish a full-automatic framework combining automatic pelvic bone marrow (BM) segmentation and radiomics analysis o...
Chapter
Trauma and orthopedic surgeries that involve fluoroscopic guidance crucially depend on the acquisition of correct anatomy-specific standard projections for monitoring and evaluating the surgical result. This implies repeated acquisitions or even continuous fluoroscopy. To reduce radiation exposure and time, we propose to automate this procedure and...
Chapter
The ability to estimate how a tumor might evolve in the future could have tremendous clinical benefits, from improved treatment decisions to better dose distribution in radiation therapy. Recent work has approached the glioma growth modeling problem via deep learning and variational inference, thus learning growth dynamics entirely from a real pati...
Chapter
Simultaneous localisation and categorization of objects in medical images, also referred to as medical object detection, is of high clinical relevance because diagnostic decisions often depend on rating of objects rather than e.g. pixels. For this task, the cumbersome and iterative process of method configuration constitutes a major research bottle...
Article
The specific role of white matter (WM) microstructure in parkinsonism among patients with schizophrenia spectrum disorders (SSD) is largely unknown. To determine whether topographical alterations of WM microstructure contribute to parkinsonism in SSD patients, we examined healthy controls (HC, n=16) and SSD patients with and without parkinsonism, a...
Preprint
Full-text available
The ability to estimate how a tumor might evolve in the future could have tremendous clinical benefits, from improved treatment decisions to better dose distribution in radiation therapy. Recent work has approached the glioma growth modeling problem via deep learning and variational inference, thus learning growth dynamics entirely from a real pati...
Article
Full-text available
The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imag...
Preprint
Full-text available
Neural Processes (NPs) are a family of conditional generative models that are able to model a distribution over functions, in a way that allows them to perform predictions at test time conditioned on a number of context points. A recent addition to this family, Convolutional Conditional Neural Processes (ConvCNP), have shown remarkable improvement...
Preprint
Simultaneous localisation and categorization of objects in medical images, also referred to as medical object detection, is of high clinical relevance because diagnostic decisions often depend on rating of objects rather than e.g. pixels. For this task, the cumbersome and iterative process of method configuration constitutes a major research bottle...
Article
Background Currently, interpretation of prostate MRI is performed qualitatively. Quantitative assessment of the mean apparent diffusion coefficient (mADC) is promising to improve diagnostic accuracy while radiomic machine learning (RML) allows to probe complex parameter spaces to identify the most promising multi-parametric models. We have previous...
Preprint
Full-text available
Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we su...
Article
Background: The potential of deep learning to support radiologist prostate magnetic resonance imaging (MRI) interpretation has been demonstrated. Purpose: The aim of this study was to evaluate the effects of increased and diversified training data (TD) on deep learning performance for detection and segmentation of clinically significant prostate...
Preprint
Full-text available
This manuscript describes the first challenge on Federated Learning, namely the Federated Tumor Segmentation (FeTS) challenge 2021. International challenges have become the standard for validation of biomedical image analysis methods. However, the actual performance of participating (even the winning) algorithms on "real-world" clinical data often...
Article
Full-text available
Accurate detection and quantification of unruptured intracranial aneurysms (UIAs) is important for rupture risk assessment and to allow an informed treatment decision to be made. Currently, 2D manual measures used to assess UIAs on Time-of-Flight magnetic resonance angiographies (TOF-MRAs) lack 3D information and there is substantial inter-observer...
Article
Full-text available
Purpose Reduction and osteosynthesis of ankle fractures is a challenging surgical procedure when it comes to the verification of the reduction result. Evaluation is conducted using intra-operative imaging of the injured ankle and depends on the expertise of the surgeon. Studies suggest that intra-individual variance of the ankle bone shape and pose...
Article
Objective: Evaluation of software tools for segmentation, quantification, and characterization of fibrotic pulmonary parenchyma changes will strengthen the role of CT as biomarkers of disease extent, evolution, and response to therapy in idiopathic pulmonary fibrosis (IPF) patients. Methods: 418 nonenhanced thin-section MDCTs of 127 IPF patients...
Preprint
Full-text available
While the importance of automatic image analysis is increasing at an enormous pace, recent meta-research revealed major flaws with respect to algorithm validation. Specifically, performance metrics are key for objective, transparent and comparative performance assessment, but relatively little attention has been given to the practical pitfalls when...
Chapter
We apply nnU-Net to the segmentation task of the BraTS 2020 challenge. The unmodified nnU-Net baseline configuration already achieves a respectable result. By incorporating BraTS-specific modifications regarding postprocessing, region-based training, a more aggressive data augmentation as well as several minor modifications to the nnU-Net pipeline...
Article
Full-text available
Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable image analysis and quantification in many applications, the design of respective specialized solutions is non-trivial and highly dependent on dataset propert...
Chapter
Image analysis is one of the most promising applications of artificial intelligence (AI) in healthcare, potentially improving prediction, diagnosis and treatment of diseases. While scientific advances in this area critically depend on the accessibility of large-volume and high-quality data, sharing data between institutions faces various ethical an...
Chapter
Guidance and quality control in orthopedic surgery increasingly relies on intra-operative fluoroscopy using a mobile C-arm. The accurate acquisition of standardized and anatomy-specific projections is essential in this process. The corresponding iterative positioning of the C-arm is error-prone and involves repeated manual acquisitions or even cont...
Chapter
The major goal of radiomics studies is the identification of predictive and reliable markers. It is, therefore, crucial to account for unwanted confounding effects that affect the radiomic features like scanning noise, annotator bias, or the used imaging device and parameter. Usually, these confounding effects are not sufficiently represented in th...
Chapter
Cardiac magnetic resonance imaging (cMRI) is an integral part of diagnosis in many heart related diseases. Recently, deep neural networks (DNN) have demonstrated successful automatic segmentation, thus alleviating the burden of time-consuming manual contouring of cardiac structures. Moreover, frameworks such as nnU-Net provide entirely automatic mo...
Chapter
Cardiac magnetic resonance imaging (cMRI) is an integral part of diagnosis in many heart related diseases. Recently, deep neural networks have demonstrated successful automatic segmentation, thus alleviating the burden of time-consuming manual contouring of cardiac structures. Moreover, frameworks such as nnU-Net provide entirely auto- matic model...
Book
In den letzten Jahren hat sich der Workshop "Bildverarbeitung für die Medizin" durch erfolgreiche Veranstaltungen etabliert. Ziel ist auch 2021 wieder die Darstellung aktueller Forschungsergebnisse und die Vertiefung der Gespräche zwischen Wissenschaftlern, Industrie und Anwendern. Die Beiträge dieses Bandes - einige davon in englischer Sprache - u...
Article
Full-text available
Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the perf...
Preprint
Image registration is the basis for many applications in the fields of medical image computing and computer assisted interventions. One example is the registration of 2D X-ray images with preoperative three-dimensional computed tomography (CT) images in intraoperative surgical guidance systems. Due to the high safety requirements in medical applica...
Article
Materials and methods: Our local ethics committee approved this retrospective monocenter study.First, a dual-time approach was assessed, for which the CNN was provided sequences of the MRI that initially depicted new MM (diagnosis MRI) as well as of a prediagnosis MRI: inclusion of only contrast-enhanced T1-weighted images (CNNdual_ce) was compare...
Article
Full-text available
Data-driven machine learning (ML) has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. Ho...
Article
The quantitative analysis of images acquired in the diagnosis and treatment of patients with brain tumors has seen a significant rise in the clinical use of computational tools. The underlying technology to the vast majority of these tools are machine learning methods and, in particular, deep learning algorithms. This review offers clinical backgro...
Preprint
Full-text available
Cardiac magnetic resonance imaging (cMRI) is an integral part of diagnosis in many heart related diseases. Recently, deep neural networks have demonstrated successful automatic segmentation, thus alleviating the burden of time-consuming manual contouring of cardiac structures. Moreover, frameworks such as nnU-Net provide entirely automatic model co...
Preprint
We apply nnU-Net to the segmentation task of the BraTS 2020 challenge. The unmodified nnU-Net baseline configuration already achieves a respectable result. By incorporating BraTS-specific modifications regarding postprocessing, region-based training, a more aggressive data augmentation as well as several minor modifications to the nnUNet pipeline w...
Article
Full-text available
PURPOSE Image analysis is one of the most promising applications of artificial intelligence (AI) in health care, potentially improving prediction, diagnosis, and treatment of diseases. Although scientific advances in this area critically depend on the accessibility of large-volume and high-quality data, sharing data between institutions faces vario...
Article
Full-text available
Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is,...
Article
There is a large body of literature linking anatomic and geometric characteristics of kidney tumors to perioperative and oncologic outcomes. Semantic segmentation of these tumors and their host kidneys is a promising tool for quantitatively characterizing these lesions, but its adoption is limited due to the manual effort required to produce high-q...
Article
Full-text available
Objectives To simulate clinical deployment, evaluate performance, and establish quality assurance of a deep learning algorithm (U-Net) for detection, localization, and segmentation of clinically significant prostate cancer (sPC), ISUP grade group ≥ 2, using bi-parametric MRI.Methods In 2017, 284 consecutive men in active surveillance, biopsy-naïve...