9.8k citations · 17.9k across the 31 of their papers we have counts for
21 papers · 1 filter
Robust Medical Instrument Segmentation Challenge 2019
Tobias Ross, Annika Reinke, Peter M. Full +47
Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting, segmenting and tr…
Reg R-CNN: Lesion Detection and Grading under Noisy Labels
Gregor N. Ramien, Paul F. Jaeger, Simon A. A. Kohl +1
For the task of concurrently detecting and categorizing objects, the medical imaging community commonly adopts methods developed on natural images. Current state-of-the-art object…
A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities
Simon A. A. Kohl, Bernardino Romera-Paredes, Klaus H. Maier-Hein +5
Medical imaging only indirectly measures the molecular identity of the tissue within each voxel, which often produces only ambiguous image evidence for target measures of interest,…
Automated Design of Deep Learning Methods for Biomedical Image Segmentation
Fabian Isensee, Paul F. Jäger, Simon A. A. Kohl +2
Biomedical imaging is a driver of scientific discovery and core component of medical care, currently stimulated by the field of deep learning. While semantic segmentation algorithm…
Automated brain extraction of multi-sequence MRI using artificial neural networks
Fabian Isensee, Marianne Schell, Irada Tursunova +10
Brain extraction is a critical preprocessing step in the analysis of MRI neuroimaging studies and influences the accuracy of downstream analyses. The majority of brain extraction a…
Combined tract segmentation and orientation mapping for bundle-specific tractography
Jakob Wasserthal, Peter Neher, Dusan Hirjak +1
While the major white matter tracts are of great interest to numerous studies in neuroscience and medicine, their manual dissection in larger cohorts from diffusion MRI tractograms…
Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection
Paul F. Jaeger, Simon A. A. Kohl, Sebastian Bickelhaupt +4
The task of localizing and categorizing objects in medical images often remains formulated as a semantic segmentation problem. This approach, however, only indirectly solves the co…
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
Fabian Isensee, Jens Petersen, Andre Klein +8
The U-Net was presented in 2015. With its straight-forward and successful architecture it quickly evolved to a commonly used benchmark in medical image segmentation. The adaptation…
No New-Net
Fabian Isensee, Philipp Kickingereder, Wolfgang Wick +2
In this paper we demonstrate the effectiveness of a well trained U-Net in the context of the BraTS 2018 challenge. This endeavour is particularly interesting given that researchers…
Domain Adaptation for Deviating Acquisition Protocols in CNN-based Lesion Classification on Diffusion-Weighted MR Images
Jennifer Kamphenkel, Paul F. Jaeger, Sebastian Bickelhaupt +8
End-to-end deep learning improves breast cancer classification on diffusion-weighted MR images (DWI) using a convolutional neural network (CNN) architecture. A limitation of CNN as…
Tract orientation mapping for bundle-specific tractography
Jakob Wasserthal, Peter F. Neher, Klaus H. Maier-Hein
While the major white matter tracts are of great interest to numerous studies in neuroscience and medicine, their manual dissection in larger cohorts from diffusion MRI tractograms…
A Probabilistic U-Net for Segmentation of Ambiguous Images
Simon A. A. Kohl, Bernardino Romera-Paredes, Clemens Meyer +6
Many real-world vision problems suffer from inherent ambiguities. In clinical applications for example, it might not be clear from a CT scan alone which particular region is cancer…
Why rankings of biomedical image analysis competitions should be interpreted with care
Lena Maier-Hein, Matthias Eisenmann, Annika Reinke +35
International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of c…
TractSeg - Fast and accurate white matter tract segmentation
Jakob Wasserthal, Peter Neher, Klaus H. Maier-Hein
The individual course of white matter fiber tracts is an important key for analysis of white matter characteristics in healthy and diseased brains. Uniquely, diffusion-weighted MRI…
Towards whole-body CT Bone Segmentation
André Klein, Jan Warszawski, Jens Hillengaß +1
Bone segmentation from CT images is a task that has been worked on for decades. It is an important ingredient to several diagnostics or treatment planning approaches and relevant t…
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge
Fabian Isensee, Philipp Kickingereder, Wolfgang Wick +2
Quantitative analysis of brain tumors is critical for clinical decision making. While manual segmentation is tedious, time consuming and subjective, this task is at the same time v…
Exploiting the potential of unlabeled endoscopic video data with self-supervised learning
Tobias Ross, David Zimmerer, Anant Vemuri +12
Surgical data science is a new research field that aims to observe all aspects of the patient treatment process in order to provide the right assistance at the right time. Due to t…
Direct White Matter Bundle Segmentation using Stacked U-Nets
Jakob Wasserthal, Peter F. Neher, Fabian Isensee +1
The state-of-the-art method for automatically segmenting white matter bundles in diffusion-weighted MRI is tractography in conjunction with streamline cluster selection. This proce…
Adversarial Networks for the Detection of Aggressive Prostate Cancer
Simon Kohl, David Bonekamp, Heinz-Peter Schlemmer +5
Semantic segmentation constitutes an integral part of medical image analyses for which breakthroughs in the field of deep learning were of high relevance. The large number of train…
Revealing Hidden Potentials of the q-Space Signal in Breast Cancer
Paul Jaeger, Sebastian Bickelhaupt, Frederik Bernd Laun +10
Mammography screening for early detection of breast lesions currently suffers from high amounts of false positive findings, which result in unnecessary invasive biopsies. Diffusion…
Image biomarker standardisation initiative
Alex Zwanenburg, Stefan Leger, Martin Vallières +1
The image biomarker standardisation initiative (IBSI) is an independent international collaboration which works towards standardising the extraction of image biomarkers from acquir…