activity
20192026
most citedMedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

35 citations · 113 across the 30 of their papers we have counts for

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Showing eess.IVShow all

8 papers · 1 filter

eess.IV2024★ 1 cited

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…

eess.IV2024★ 2 cited

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.…

eess.IV2023★ 3 cited

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…

eess.IV2023★ 2 cited

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…

eess.IV2023★ 19 cited

CellViT: Vision Transformers for Precise Cell Segmentation and Classification

Fabian Hörst, Moritz Rempe, Lukas Heine +8

Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images are important clinical tasks and crucial for a wide range of applications. However, it is a c…

eess.IV2022★ 2 cited

Valuing Vicinity: Memory attention framework for context-based semantic segmentation in histopathology

Oliver Ester, Fabian Hörst, Constantin Seibold +9

The segmentation of histopathological whole slide images into tumourous and non-tumourous types of tissue is a challenging task that requires the consideration of both local and gl…