Estimating Nonplanar Flow from 2D Motion-blurred Widefield Microscopy Images via Deep Learning

TL;DR

This paper introduces a deep learning approach to estimate 3D particle movement in microscopy images from standard light conditions, reducing the need for high-speed imaging and phototoxicity.

Contribution

The novel method predicts out-of-plane particle motion from single images by analyzing motion blur with a neural network, enabling safer and more accessible microscopy.

Findings

Achieved a regression coefficient of 0.92 with ground truth data.

Enabled estimation of 3D flow from single 2D images.

Reduced reliance on high-speed cameras and intense illumination.

Abstract

Optical flow is a method aimed at predicting the movement velocity of any pixel in the image and is used in medicine and biology to estimate flow of particles in organs or organelles. However, a precise optical flow measurement requires images taken at high speed and low exposure time, which induces phototoxicity due to the increase in illumination power. We are looking here to estimate the three-dimensional movement vector field of moving out-of-plane particles using normal light conditions and a standard microscope camera. We present a method to predict, from a single textured wide-field microscopy image, the movement of out-of-plane particles using the local characteristics of the motion blur. We estimated the velocity vector field from the local estimation of the blur model parameters using an deep neural network and achieved a prediction with a regression coefficient of 0.92 between the ground truth simulated vector field and the output of the network. This method could enable microscopists to gain insights about the dynamic properties of samples without the need for high-speed cameras or high-intensity light exposure.