A registration error estimation framework for correlative imaging

3D correlative single-cell imaging utilizing fluorescence and refractive index tomography

Cells alter the path of light, a fact that leads to well-known aberrations in single cell or tissue imaging. Optical diffraction tomography (ODT) measures the biophysical property that causes these aberrations, the refractive index (RI). ODT is complementary to fluorescence imaging and does not require any markers. The present study introduces RI and fluorescence tomography with optofluidic rotation (RAFTOR) of suspended cells, quantifying the intracellular RI distribution and colocalizing it with fluorescence in 3D. The technique is validated with cell phantoms and used to confirm a lower nuc…

Guided-deconvolution for Correlative Light and Electron Microscopy

Correlative light and electron microscopy is a powerful tool to study the internal structure of cells. It combines the mutual benefit of correlating light (LM) and electron (EM) microscopy information. However, the classical approach of overlaying LM onto EM images to assign functional to structural information is hampered by the large discrepancy in structural detail visible in the LM images. This paper aims at investigating an optimized approach which we call EM-guided deconvolution. It attempts to automatically assign fluorescence-labelled structures to details visible in the EM image to br…

NeuReg：人类和小鼠大脑上的域不变三维图像配准

NeuReg: Domain-invariant 3D Image Registration on Human and Mouse Brains

NeuReg提出一种神经启发式三维图像配准架构，利用Swin Transformer编码器生成域无关表示，在人类和小鼠脑数据集上超越现有基线，实现跨域高性能配准。

Deep Learning in Medical Image Registration: A Survey

The establishment of image correspondence through robust image registration is critical to many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring, and is a very challenging problem. Since the beginning of the recent deep learning renaissance, the medical imaging research community has developed deep learning based approaches and achieved the state-of-the-art in many applications, including image registration. The rapid adoption of deep learning for image registration applications over the past few years necessitates a comprehensive summary and outlook, whic…

生物力学引导的图像配准用于患者特异性主动脉瓣应变分析

Biomechanically Informed Image Registration for Patient-Specific Aortic Valve Strain Analysis

本文提出一种结合有限元法的图像配准方法，用于患者特异性主动脉瓣应变分析，提高生物力学评估的准确性。

A Highly Accurate Model Based Registration Method for FIB-SEM Images of Neurons

Focused Ion Beam Scanning Electron Microscope (FIB-SEM) imaging is a technique that image materials section-by-section at nano-resolution, e.g.,5 nanometer width voxels. FIB-SEM is well suited for imaging ultrastructures in cells. Unfortunately, typical setups will introduce a slight sub-pixel translation from section to section typically referred to as drift. Over multiple sections, drift compound to skew distance measures and geometric structures significantly from the pre-imaged stage. Popular correction approaches often involve standard image registration methods available in packages such…