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From the 1 of 44 linked papers with an AI index.

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20242026
most citedThe TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography

12 citations · 13 across the 14 of their papers we have counts for

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eess.IV2026

TimeFlow: Temporal Conditioning for Longitudinal Brain MRI Registration and Aging Analysis

Bailiang Jian, Jiazhen Pan, Yitong Li +5

Longitudinal brain analysis is essential for understanding healthy aging and identifying pathological deviations. Longitudinal registration of sequential brain MRI underpins such a…

eess.IV2026

MedFuncta: A Unified Framework for Learning Efficient Medical Neural Fields

Paul Friedrich, Florentin Bieder, Julian McGinnis +3

Research in medical imaging primarily focuses on discrete data representations that poorly scale with grid resolution and fail to capture the often continuous nature of the underly…

eess.IV2025

Disentangling Progress in Medical Image Registration: Beyond Trend-Driven Architectures towards Domain-Specific Strategies

Bailiang Jian, Jiazhen Pan, Rohit Jena +5

Medical image registration drives quantitative analysis across organs, modalities, and patient populations. Recent deep learning methods often combine low-level "trend-driven" comp…

eess.IV2025

MM-DINOv2: Adapting Foundation Models for Multi-Modal Medical Image Analysis

Daniel Scholz, Ayhan Can Erdur, Viktoria Ehm +4

Vision foundation models like DINOv2 demonstrate remarkable potential in medical imaging despite their origin in natural image domains. However, their design inherently works best…

eess.IV2025

Contrastive Anatomy-Contrast Disentanglement: A Domain-General MRI Harmonization Method

Daniel Scholz, Ayhan Can Erdur, Robbie Holland +4

Magnetic resonance imaging (MRI) is an invaluable tool for clinical and research applications. Yet, variations in scanners and acquisition parameters cause inconsistencies in image…

eess.IV2025

VIBESegmentator: Full Body MRI Segmentation for the NAKO and UK Biobank

Robert Graf, Paul-Sören Platzek, Evamaria Olga Riedel +17

Objectives: To present a publicly available deep learning-based torso segmentation model that provides comprehensive voxel-wise coverage, including delineations that extend to the…