65 citations · 104 across the 30 of their papers we have counts for
10 papers · 1 filter
GLOW-FDG: Generalized cancer LesiOn Whole-body segmentation model for F-FDG-PET/CT
Maksym Fritsak, Maximilian Rokuss, Hubert S. Gabryś +10
Whole-body fluorodeoxyglucose positron emission tomography combined with computed tomography is widely used in cancer care, but manual lesion delineation is slow, subjective, and d…
Promptable Longitudinal Lesion Segmentation in Whole-Body CT
Yannick Kirchhoff, Maximilian Rokuss, Fabian Isensee +1
Accurate segmentation of lesions in longitudinal whole-body CT is essential for monitoring disease progression and treatment response. While automated methods benefit from incorpor…
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…
Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge
Gongning Luo, Mingwang Xu, Hongyu Chen +34
Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a ne…
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…
Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting
Maximilian Rokuss, Yannick Kirchhoff, Saikat Roy +9
Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across tim…
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…
Skeleton Recall Loss for Connectivity Conserving and Resource Efficient Segmentation of Thin Tubular Structures
Yannick Kirchhoff, Maximilian R. Rokuss, Saikat Roy +8
Accurately segmenting thin tubular structures, such as vessels, nerves, roads or concrete cracks, is a crucial task in computer vision. Standard deep learning-based segmentation lo…
SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model
Saikat Roy, Tassilo Wald, Gregor Koehler +5
Foundation models have taken over natural language processing and image generation domains due to the flexibility of prompting. With the recent introduction of the Segment Anything…