bibtext: @InProceedings{ Kir_In_MICCAI2024,
                   author = { Kirkegaard, Julius B. and Kutuzov, Nikolay P. and Netterstrøm, Rasmus and Darkner, Sune and Lauritzen, Martin and Lauze, François },
                   title = { { In vivo deep learning estimation of diffusion coefficients of nanoparticles } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15002 },
                   month = {October},
                   pages = { pending },
                }
abstract: Understanding the transport of molecules in the brain \emph{in vivo} is the key to learning how the brain regulates its metabolism, how brain pathologies develop, and how most of the developed brain-targeted drugs fail. Two-photon microscopy – the main tool for \emph{in vivo} brain imaging – achieves sub-micrometer resolution and high image contrast when imaging cells, blood vessels, and other microscopic structures. However, images of small and fast-moving objects, e.g. nanoparticles, are ill-suited for analysis of transport with standard methods, e.g. super-localization, because of (i) low photon budgets resulting in noisy images; (ii) severe motion blur due to slow pixel-by-pixel image acquisition by t-photon microscopy; and (iii) high density of tracked objects, preventing their individual localization.
  Here, we developed a deep learning-based estimator of diffusion coefficients of nanoparticles directly from movies recorded with two-photon microscopy \emph{in vivo}.
  We’ve benchmarked the method with synthetic data, model experimental data (nanoparticles in water), and \emph{in vivo} data (nanoparticles in the brain). 
  Deep Learning robustly estimates the diffusion coefficient of nanoparticles from movies with severe motion blur and movies with high nanoparticle densities, where, in contrast to the classic algorithms, the deep learning estimator’s accuracy improves with increasing density. 
  As a result, the deep learning estimator facilitates the estimation of diffusion coefficients of nanoparticles in the brain \emph{in vivo}, where the existing estimators fail.
title: In vivo deep learning estimation of diffusion coefficients of nanoparticles
authors: Kirkegaard, Julius B. Kutuzov, Nikolay P. Netterstrøm, Rasmus Darkner, Sune Lauritzen, Martin Lauze, François