386 citations · 393 across the 12 of their papers we have counts for
14 papers
VesselTok: Tokenizing Vessel-like 3D Biomedical Graph Representations for Reconstruction and Generation
Chinmay Prabhakar, Bastian Wittmann, Tamaz Amiranashvili +6
Spatial graphs provide a lightweight and elegant representation of curvilinear anatomical structures such as blood vessels, lung airways, and neuronal networks. Accurately modeling…
CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography
Murong Xu, Tamaz Amiranashvili, Fernando Navarro +30
Accurate delineation of anatomical structures in volumetric CT scans is crucial for diagnosis and treatment planning. While AI has advanced automated segmentation, current approach…
Semantically Consistent Discrete Diffusion for 3D Biological Graph Modeling
Chinmay Prabhakar, Suprosanna Shit, Tamaz Amiranashvili +2
3D spatial graphs play a crucial role in biological and clinical research by modeling anatomical networks such as blood vessels,neurons, and airways. However, generating 3D biologi…
Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling
Michal Balcerak, Tamaz Amiranashvili, Antonio Terpin +5
Current state-of-the-art generative models map noise to data distributions by matching flows or scores. A key limitation of these models is their inability to readily integrate ava…
vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation
Bastian Wittmann, Yannick Wattenberg, Tamaz Amiranashvili +2
Segmenting 3D blood vessels is a critical yet challenging task in medical image analysis. This is due to significant imaging modality-specific variations in artifacts, vascular pat…
Physics-Regularized Multi-Modal Image Assimilation for Brain Tumor Localization
Michal Balcerak, Tamaz Amiranashvili, Andreas Wagner +7
Physical models in the form of partial differential equations serve as important priors for many under-constrained problems. One such application is tumor treatment planning, which…
3D Vessel Graph Generation Using Denoising Diffusion
Chinmay Prabhakar, Suprosanna Shit, Fabio Musio +5
Blood vessel networks, represented as 3D graphs, help predict disease biomarkers, simulate blood flow, and aid in synthetic image generation, relevant in both clinical and pre-clin…
QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge
Hongwei Bran Li, Fernando Navarro, Ivan Ezhov +77
Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a sign…
Probabilistic Contrastive Learning with Explicit Concentration on the Hypersphere
Hongwei Bran Li, Cheng Ouyang, Tamaz Amiranashvili +3
Self-supervised contrastive learning has predominantly adopted deterministic methods, which are not suited for environments characterized by uncertainty and noise. This paper intro…
Simulation-Based Segmentation of Blood Vessels in Cerebral 3D OCTA Images
Bastian Wittmann, Lukas Glandorf, Johannes C. Paetzold +4
Segmentation of blood vessels in murine cerebral 3D OCTA images is foundational for in vivo quantitative analysis of the effects of neurovascular disorders, such as stroke or Alzhe…
A Domain-specific Perceptual Metric via Contrastive Self-supervised Representation: Applications on Natural and Medical Images
Hongwei Bran Li, Chinmay Prabhakar, Suprosanna Shit +7
Quantifying the perceptual similarity of two images is a long-standing problem in low-level computer vision. The natural image domain commonly relies on supervised learning, e.g.,…
Landmark-free Statistical Shape Modeling via Neural Flow Deformations
David Lüdke, Tamaz Amiranashvili, Felix Ambellan +3
Statistical shape modeling aims at capturing shape variations of an anatomical structure that occur within a given population. Shape models are employed in many tasks, such as shap…
A for-loop is all you need. For solving the inverse problem in the case of personalized tumor growth modeling
Ivan Ezhov, Marcel Rosier, Lucas Zimmer +12
Solving the inverse problem is the key step in evaluating the capacity of a physical model to describe real phenomena. In medical image computing, it aligns with the classical them…
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Anjany Sekuboyina, Malek E. Husseini, Amirhossein Bayat +66
Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit cl…