9.8k citations · 18.5k across the 47 of their papers we have counts for
55 papers
TriALS: Triphasic-Aided Liver Lesion Segmentation Benchmark in Non-Contrast CT
Marawan Elbatel, Mohamed Ghonim, Jiaji Mao +62
Automated segmentation of liver lesions on non-contrast computed tomography (NCCT) is clinically important but fundamentally challenging, particularly in low-resource settings acro…
The autoPET3 Challenge: Automated Lesion Segmentation in Whole-Body PET/CT $\unicode{x2013}$ Multitracer Multicenter Generalization
Jakob Dexl, Katharina Jeblick, Andreas Mittermeier +27
We report the design and results of the third autoPET challenge (MICCAI 2024), which benchmarked automated lesion segmentation in whole-body PET/CT under a compositional generaliza…
nnActive: A Framework for Evaluation of Active Learning in 3D Biomedical Segmentation
Carsten T. Lüth, Jeremias Traub, Kim-Celine Kahl +6
Semantic segmentation is crucial for various biomedical applications, yet its reliance on large annotated datasets presents a bottleneck due to the high cost and specialized expert…
Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge
Muhammad Imran, Jonathan R. Krebs, Vishal Balaji Sivaraman +60
Multi-class segmentation of the aorta in computed tomography angiography (CTA) scans is essential for diagnosing and planning complex endovascular treatments for patients with aort…
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang +50
How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified…
Decoupling Semantic Similarity from Spatial Alignment for Neural Networks
Tassilo Wald, Constantin Ulrich, Gregor Köhler +6
What representation do deep neural networks learn? How similar are images to each other for neural networks? Despite the overwhelming success of deep learning methods key questions…
From FDG to PSMA: A Hitchhiker's Guide to Multitracer, Multicenter Lesion Segmentation in PET/CT Imaging
Maximilian Rokuss, Balint Kovacs, Yannick Kirchhoff +4
Automated lesion segmentation in PET/CT scans is crucial for improving clinical workflows and advancing cancer diagnostics. However, the task is challenging due to physiological va…
Data-Centric Strategies for Overcoming PET/CT Heterogeneity: Insights from the AutoPET III Lesion Segmentation Challenge
Balint Kovacs, Shuhan Xiao, Maximilian Rokuss +3
The third autoPET challenge introduced a new data-centric task this year, shifting the focus from model development to improving metastatic lesion segmentation on PET/CT images thr…
Mitigating False Predictions In Unreasonable Body Regions
Constantin Ulrich, Catherine Knobloch, Julius C. Holzschuh +7
Despite considerable strides in developing deep learning models for 3D medical image segmentation, the challenge of effectively generalizing across diverse image distributions pers…
Revisiting 3D Medical Scribble Supervision: Benchmarking Beyond Cardiac Segmentation
Karol Gotkowski, Klaus H. Maier-Hein, Fabian Isensee
Scribble supervision has emerged as a promising approach for reducing annotation costs in medical 3D segmentation by leveraging sparse annotations instead of voxel-wise labels. Whi…
RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement
Gregor Koehler, Tassilo Wald, Constantin Ulrich +6
Despite the remarkable success of deep learning systems over the last decade, a key difference still remains between neural network and human decision-making: As humans, we cannot…
Anatomy-informed Data Augmentation for Enhanced Prostate Cancer Detection
Balint Kovacs, Nils Netzer, Michael Baumgartner +17
Data augmentation (DA) is a key factor in medical image analysis, such as in prostate cancer (PCa) detection on magnetic resonance images. State-of-the-art computer-aided diagnosis…
Look Ma, no code: fine tuning nnU-Net for the AutoPET II challenge by only adjusting its JSON plans
Fabian Isensee, Klaus H. Maier-Hein
We participate in the AutoPET II challenge by modifying nnU-Net only through its easy to understand and modify 'nnUNetPlans.json' file. By switching to a UNet with residual encoder…
Exploring new ways: Enforcing representational dissimilarity to learn new features and reduce error consistency
Tassilo Wald, Constantin Ulrich, Fabian Isensee +4
Independently trained machine learning models tend to learn similar features. Given an ensemble of independently trained models, this results in correlated predictions and common f…
The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT
Nicholas Heller, Fabian Isensee, Dasha Trofimova +42
This paper presents the challenge report for the 2021 Kidney and Kidney Tumor Segmentation Challenge (KiTS21) held in conjunction with the 2021 international conference on Medical…
Transformer Utilization in Medical Image Segmentation Networks
Saikat Roy, Gregor Koehler, Michael Baumgartner +4
Owing to success in the data-rich domain of natural images, Transformers have recently become popular in medical image segmentation. However, the pairing of Transformers with convo…
Why is the winner the best?
Matthias Eisenmann, Annika Reinke, Vivienn Weru +122
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to in…
MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation
Constantin Ulrich, Fabian Isensee, Tassilo Wald +3
The medical imaging community generates a wealth of datasets, many of which are openly accessible and annotated for specific diseases and tasks such as multi-organ or lesion segmen…
MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation
Saikat Roy, Gregor Koehler, Constantin Ulrich +5
There has been exploding interest in embracing Transformer-based architectures for medical image segmentation. However, the lack of large-scale annotated medical datasets make achi…
Understanding metric-related pitfalls in image analysis validation
Annika Reinke, Minu D. Tizabi, Michael Baumgartner +75
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation int…
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization
Carsten T. Lüth, David Zimmerer, Gregor Koehler +4
Unsupervised anomaly detection in medical imaging aims to detect and localize arbitrary anomalies without requiring annotated anomalous data during training. Often, this is achieve…
ParticleSeg3D: A Scalable Out-of-the-Box Deep Learning Segmentation Solution for Individual Particle Characterization from Micro CT Images in Mineral Processing and Recycling
Karol Gotkowski, Shuvam Gupta, Jose R. A. Godinho +3
Minerals, metals, and plastics are indispensable for a functioning modern society. Yet, their supply is limited causing a need for optimizing ore extraction and recuperation from r…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Extending nnU-Net is all you need
Fabian Isensee, Constantin Ulrich, Tassilo Wald +1
Semantic segmentation is one of the most popular research areas in medical image computing. Perhaps surprisingly, despite its conceptualization dating back to 2018, nnU-Net continu…