TrackSegNet: a tool for trajectory segmentation into diffusive states using supervised deep learning-Reference-Cited by-同舟云学术

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TrackSegNet: a tool for trajectory segmentation into diffusive states using supervised deep learning

Published:2024-06-04 Issue:98 Volume:9 Page:6157
ISSN:2475-9066
Container-title:Journal of Open Source Software
language:
Short-container-title:JOSS

Author:

Kabbech Hélène^ORCID,Smal Ihor^ORCID

Publisher

The Open Journal

Link

https://joss.theoj.org/papers/10.21105/joss.06157.pdf

Reference8 articles.

1. Particle mobility analysis using deep learning and the moment scaling spectrum;Arts;Scientific reports,2019

2. Objective comparison of methods to decode anomalous diffusion;Muñoz-Gil;Nature communications,2021

3. Anomalous diffusion models and their properties: Non-stationarity, non-ergodicity, and ageing at the centenary of single particle tracking;Metzler;Physical Chemistry Chemical Physics,2014

4. Fractional brownian motion: A maximum likelihood estimator and its application to image texture;Lundahl;IEEE transactions on Medical Imaging,1986

5. Robust model-based analysis of single-particle tracking experiments with spot-on;Hansen;Elife,2018

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