Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

multimodars: A Rust-powered toolkit for multi-modality cardiac image fusion and registration

Domaine:

healthcare

Type de record:

papersoftware
Créateur:
StaIliMokKaz
Hôte:avatar
Combining complementary imaging modalities is critical to build reliable 3D coronary models: intravascular imaging gives sub-millimetre resolution but limited whole-vessel context, while CCTA supplies 3D geometry but suffers from limited spatial resolution and artefacts (e.g., blooming). Prior work demonstrated intravascular/CCTA fusion, yet no open, flexible toolkit is tailored for multi-state analysis (rest/stress, pre-/post-stenting) while offering deterministic behaviour, high performance, and easy pipeline integration. multimodars addresses this gap with deterministic alignment algorithms, a compact NumPy-centred data model, and an optimised Rust backend suitable for scalable, reproducible experiments. The package accepts CSV/NumPy inputs including data formats produced by the AIVUS-CAA software

Visit

arxiv.org

Tasks

computer vision

Tags

Computer Vision and Pattern RecognitionMedical Physics

Similaires

Deep Visible and Thermal Image Fusion with Cross-Modality Feature Selection for Pedestrian DetectionSingle-modality and joint fusion deep learning for diabetic retinopathy diagnosisDeep Learning for Satellite Image FusionFast Predictive Image RegistrationRegistration of KUWNSr, a wheat stem rust nested association mapping populationMulti‐modality‐based Arabic sign language recognition

Deep Visible and Thermal Image Fusion with Cross-Modality Feature Selection for Pedestrian Detection

Part 2: AI International audience This paper proposes a deep RGB and thermal image fu

Single-modality and joint fusion deep learning for diabetic retinopathy diagnosis

Single-modality and joint fusion deep learning for diabetic retinopathy diagnosis

Poster presented at the Deep Learning Indaba 2022 by SARA EL-ATEIF

Deep Learning for Satellite Image Fusion

Deep Learning for Satellite Image Fusion

Poster presented at the Deep Learning Indaba 2022 by Tayeb Benzenati

Fast Predictive Image Registration

We present a method to predict image deformations based on patch-wise image appearance. Specifically

Registration of KUWNSr, a wheat stem rust nested association mapping population

Abstract A spring wheat nested associated mapping (NAM) population, KUWNSr (Kenyan and U.S. wheat n

Multi‐modality‐based Arabic sign language recognition

With the increase in the number of deaf‐mute people in the Arab world and the lack of Arabic sign la