69 citations · 126 across the 27 of their papers we have counts for
29 papers
BiCC: Bidirectional Connected-Component Loss for Instance-Aware Segmentation
Luc Bouteille, Frederic Jonske, Jens Kleesiek +1
Common segmentation losses aggregate errors voxel-wise, so lesions influence the objective in proportion to their volume, giving small but clinically critical lesions disproportion…
Case2Flow: Bridging Patient Cases and Guideline Flowcharts through Multimodal Retrieval
Jiale Wei, Yufan Chen, Alexander Jaus +5
Medical guidelines encode rich, evidence-based decision logic, yet the specific decision artifact a clinician needs is hard to locate within a guideline, let alone across guideline…
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…
VS-DDPM: Efficient Low-Cost Diffusion Model for Medical Modality Translation
Nikoo Moradi, Gijs Luijten, Behrus Hinrichs-Puladi +4
Diffusion models produce high-quality synthetic data but suffer from slow inference. We propose 3D Variable-Step Denoising Diffusion Probabilistic Model (VS-DDPM) a framework engin…
Learning to Look Closer: A New Instance-Wise Loss for Small Cerebral Lesion Segmentation
Luc Bouteille, Alexander Jaus, Jens Kleesiek +2
Traditional loss functions in medical image segmentation, such as Dice, often under-segment small lesions because their small relative volume contributes negligibly to the overall…
GRASPing Anatomy to Improve Pathology Segmentation
Keyi Li, Alexander Jaus, Jens Kleesiek +1
Radiologists rely on anatomical understanding to accurately delineate pathologies, yet most current deep learning approaches use pure pattern recognition and ignore the anatomical…
Automatic Fine-grained Segmentation-assisted Report Generation
Frederic Jonske, Constantin Seibold, Osman Alperen Koras +6
Reliable end-to-end clinical report generation has been a longstanding goal of medical ML research. The end goal for this process is to alleviate radiologists' workloads and provid…
Good Enough? An Investigation on the Impact of Label Quality in Large-Scale Medical Datasets
Alexander Jaus, Zdravko Marinov, Constantin Seibold +4
Manually refining radiological segmentation masks is highly resource-intensive. To determine when this expert commitment is truly justified for the training of segmentation models,…
Foreign object segmentation in chest x-rays through anatomy-guided shape insertion
Constantin Seibold, Hamza Kalisch, Lukas Heine +2
In this paper, we tackle the challenge of instance segmentation for foreign objects in chest radiographs, commonly seen in postoperative follow-ups with stents, pacemakers, or inge…
Every Component Counts: Rethinking the Measure of Success for Medical Semantic Segmentation in Multi-Instance Segmentation Tasks
Alexander Jaus, Constantin Seibold, Simon Reiß +6
We present Connected-Component~(CC)-Metrics, a novel semantic segmentation evaluation protocol, targeted to align existing semantic segmentation metrics to a multi-instance detecti…
LIMIS: Towards Language-based Interactive Medical Image Segmentation
Lena Heinemann, Alexander Jaus, Zdravko Marinov +4
Within this work, we introduce LIMIS: The first purely language-based interactive medical image segmentation model. We achieve this by adapting Grounded SAM to the medical domain a…
De-Identification of Medical Imaging Data: A Comprehensive Tool for Ensuring Patient Privacy
Moritz Rempe, Lukas Heine, Constantin Seibold +2
Medical data employed in research frequently comprises sensitive patient health information (PHI), which is subject to rigorous legal frameworks such as the General Data Protection…
Towards Synthetic Data Generation for Improved Pain Recognition in Videos under Patient Constraints
Jonas Nasimzada, Jens Kleesiek, Ken Herrmann +2
Recognizing pain in video is crucial for improving patient-computer interaction systems, yet traditional data collection in this domain raises significant ethical and logistical ch…
Autopet III challenge: Incorporating anatomical knowledge into nnUNet for lesion segmentation in PET/CT
Hamza Kalisch, Fabian Hörst, Ken Herrmann +2
Lesion segmentation in PET/CT imaging is essential for precise tumor characterization, which supports personalized treatment planning and enhances diagnostic precision in oncology.…
Spacewalker: Traversing Representation Spaces for Fast Interactive Exploration and Annotation of Unstructured Data
Lukas Heine, Fabian Hörst, Jana Fragemann +6
In industries such as healthcare, finance, and manufacturing, analysis of unstructured textual data presents significant challenges for analysis and decision making. Uncovering pat…
Anatomy-guided Pathology Segmentation
Alexander Jaus, Constantin Seibold, Simon Reiß +7
Pathological structures in medical images are typically deviations from the expected anatomy of a patient. While clinicians consider this interplay between anatomy and pathology, r…
Rethinking Annotator Simulation: Realistic Evaluation of Whole-Body PET Lesion Interactive Segmentation Methods
Zdravko Marinov, Moon Kim, Jens Kleesiek +1
Interactive segmentation plays a crucial role in accelerating the annotation, particularly in domains requiring specialized expertise such as nuclear medicine. For example, annotat…
Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images
Matthias Hadlich, Zdravko Marinov, Moon Kim +3
Deep learning has revolutionized the accurate segmentation of diseases in medical imaging. However, achieving such results requires training with numerous manual voxel annotations.…
Towards Unifying Anatomy Segmentation: Automated Generation of a Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelines
Alexander Jaus, Constantin Seibold, Kelsey Hermann +5
In this study, we present a method for generating automated anatomy segmentation datasets using a sequential process that involves nnU-Net-based pseudo-labeling and anatomy-guided…
Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling
Constantin Seibold, Alexander Jaus, Matthias A. Fink +5
Purpose: Interpreting chest radiographs (CXR) remains challenging due to the ambiguity of overlapping structures such as the lungs, heart, and bones. To address this issue, we prop…
Mirror U-Net: Marrying Multimodal Fission with Multi-task Learning for Semantic Segmentation in Medical Imaging
Zdravko Marinov, Simon Reiß, David Kersting +2
Positron Emission Tomography (PET) and Computer Tomography (CT) are routinely used together to detect tumors. PET/CT segmentation models can automate tumor delineation, however, cu…
Guiding the Guidance: A Comparative Analysis of User Guidance Signals for Interactive Segmentation of Volumetric Images
Zdravko Marinov, Rainer Stiefelhagen, Jens Kleesiek
Interactive segmentation reduces the annotation time of medical images and allows annotators to iteratively refine labels with corrective interactions, such as clicks. While existi…
Multimodal Interactive Lung Lesion Segmentation: A Framework for Annotating PET/CT Images based on Physiological and Anatomical Cues
Verena Jasmin Hallitschke, Tobias Schlumberger, Philipp Kataliakos +6
Recently, deep learning enabled the accurate segmentation of various diseases in medical imaging. These performances, however, typically demand large amounts of manual voxel annota…
AutoPET Challenge: Combining nn-Unet with Swin UNETR Augmented by Maximum Intensity Projection Classifier
Lars Heiliger, Zdravko Marinov, Max Hasin +9
Tumor volume and changes in tumor characteristics over time are important biomarkers for cancer therapy. In this context, FDG-PET/CT scans are routinely used for staging and re-sta…