DeepReg: a deep learning toolkit for medical image registration

Introduction to Medical Image Registration with DeepReg, Between Old and New

This document outlines a tutorial to get started with medical image registration using the open-source package DeepReg. The basic concepts of medical image registration are discussed, linking classical methods to newer methods using deep learning. Two iterative, classical algorithms using optimisation and one learning-based algorithm using deep learning are coded step-by-step using DeepReg utilities, all with real, open-accessible, medical data.

初始化对个体内儿童脑部MR图像配准的影响：SyN ANTs与基于深度学习方法的比较分析

Impact of Initialization on Intra-subject Pediatric Brain MR Image Registration: A Comparative Analysis between SyN ANTs and Deep Learning-Based Approaches

本研究比较了SyN ANTs与基于深度学习的配准方法在儿童脑部个体内配准中的性能，发现深度学习在Dice分数上略优且速度更快，但SyN ANTs无需大量训练仍保持稳健和可泛化，方法选择应取决于具体配准需求。

multimodars：基于 Rust 的多模态心脏图像融合与配准工具包

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

multimodars 是一个基于 Rust 的工具包，通过确定性配准算法和优化的后端，实现多模态心脏图像（血管内成像与 CCTA）的融合与配准，支持多状态分析，提供高性能和可重复性。

Fast and Robust Symmetric Image Registration Based on Distances Combining Intensity and Spatial Information

Intensity-based image registration approaches rely on similarity measures to guide the search for geometric correspondences with high affinity between images. The properties of the used measure are vital for the robustness and accuracy of the registration. In this study a symmetric, intensity interpolation-free, affine registration framework based on a combination of intensity and spatial information is proposed. The excellent performance of the framework is demonstrated on a combination of synthetic tests, recovering known transformations in the presence of noise, and real applications in bio…

SenseCare: A Research Platform for Medical Image Informatics and Interactive 3D Visualization

Clinical research on smart health has an increasing demand for intelligent and clinic-oriented medical image computing algorithms and platforms that support various applications. To this end, we have developed SenseCare research platform, which is designed to facilitate translational research on intelligent diagnosis and treatment planning in various clinical scenarios. To enable clinical research with Artificial Intelligence (AI), SenseCare provides a range of AI toolkits for different tasks, including image segmentation, registration, lesion and landmark detection from various image modaliti…