U-Net 3+ for anomalous diffusion analysis enhanced with mixture estimates (U-AnD-ME) in particle-tracking data
Biophysical processes within living systems rely on encounters and interactions between molecules in complex environments such as cells. They are often described by anomalous diffusion transport. Recent advances in single-molecule microscopy and particle-tracking techniques have yielded an abundance...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | JPhys Photonics |
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| Online Access: | https://doi.org/10.1088/2515-7647/adf9aa |
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| author | Solomon Asghar Ran Ni Giorgio Volpe |
| author_facet | Solomon Asghar Ran Ni Giorgio Volpe |
| author_sort | Solomon Asghar |
| collection | DOAJ |
| description | Biophysical processes within living systems rely on encounters and interactions between molecules in complex environments such as cells. They are often described by anomalous diffusion transport. Recent advances in single-molecule microscopy and particle-tracking techniques have yielded an abundance of data in the form of videos and trajectories that contain critical information about these biologically significant processes. However, standard approaches for characterizing anomalous diffusion from these measurements often struggle in cases of practical interest, e.g. due to short, noisy trajectories. Fully exploiting this data therefore requires the development of advanced analysis methods—a core goal at the heart of the recent international Anomalous Diffusion (AnDi) Challenges. Here, we introduce a novel machine-learning framework, U-net 3+ for anomalous diffusion analysis enhanced with mixture estimates (U-AnD-ME), that applies a U-Net 3+ based neural network alongside Gaussian mixture models to enable highly accurate characterisation of single-particle tracking data. In the 2024 AnDi challenge, U-AnD-ME outperformed all other participating methods for the analysis of two-dimensional anomalous diffusion trajectories at both single-trajectory and ensemble levels. Using a large dataset inspired by the Challenge and experimental trajectories, we further characterize the performance of U-AnD-ME in segmenting trajectories and inferring anomalous diffusion properties. |
| format | Article |
| id | doaj-art-6db8b68bb542402f83a78aed30e681b5 |
| institution | Kabale University |
| issn | 2515-7647 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | JPhys Photonics |
| spelling | doaj-art-6db8b68bb542402f83a78aed30e681b52025-08-21T06:57:46ZengIOP PublishingJPhys Photonics2515-76472025-01-017404500510.1088/2515-7647/adf9aaU-Net 3+ for anomalous diffusion analysis enhanced with mixture estimates (U-AnD-ME) in particle-tracking dataSolomon Asghar0Ran Ni1https://orcid.org/0000-0001-9478-0674Giorgio Volpe2https://orcid.org/0000-0001-9993-5348Department of Chemistry, University College London , 20 Gordon Street, WC1H 0AJ London, United Kingdom; School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University , Singapore 639798, SingaporeSchool of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University , Singapore 639798, SingaporeDepartment of Chemistry, University College London , 20 Gordon Street, WC1H 0AJ London, United KingdomBiophysical processes within living systems rely on encounters and interactions between molecules in complex environments such as cells. They are often described by anomalous diffusion transport. Recent advances in single-molecule microscopy and particle-tracking techniques have yielded an abundance of data in the form of videos and trajectories that contain critical information about these biologically significant processes. However, standard approaches for characterizing anomalous diffusion from these measurements often struggle in cases of practical interest, e.g. due to short, noisy trajectories. Fully exploiting this data therefore requires the development of advanced analysis methods—a core goal at the heart of the recent international Anomalous Diffusion (AnDi) Challenges. Here, we introduce a novel machine-learning framework, U-net 3+ for anomalous diffusion analysis enhanced with mixture estimates (U-AnD-ME), that applies a U-Net 3+ based neural network alongside Gaussian mixture models to enable highly accurate characterisation of single-particle tracking data. In the 2024 AnDi challenge, U-AnD-ME outperformed all other participating methods for the analysis of two-dimensional anomalous diffusion trajectories at both single-trajectory and ensemble levels. Using a large dataset inspired by the Challenge and experimental trajectories, we further characterize the performance of U-AnD-ME in segmenting trajectories and inferring anomalous diffusion properties.https://doi.org/10.1088/2515-7647/adf9aaparticle trackingmachine learningmicroscopy data analysisanomalous diffusion |
| spellingShingle | Solomon Asghar Ran Ni Giorgio Volpe U-Net 3+ for anomalous diffusion analysis enhanced with mixture estimates (U-AnD-ME) in particle-tracking data JPhys Photonics particle tracking machine learning microscopy data analysis anomalous diffusion |
| title | U-Net 3+ for anomalous diffusion analysis enhanced with mixture estimates (U-AnD-ME) in particle-tracking data |
| title_full | U-Net 3+ for anomalous diffusion analysis enhanced with mixture estimates (U-AnD-ME) in particle-tracking data |
| title_fullStr | U-Net 3+ for anomalous diffusion analysis enhanced with mixture estimates (U-AnD-ME) in particle-tracking data |
| title_full_unstemmed | U-Net 3+ for anomalous diffusion analysis enhanced with mixture estimates (U-AnD-ME) in particle-tracking data |
| title_short | U-Net 3+ for anomalous diffusion analysis enhanced with mixture estimates (U-AnD-ME) in particle-tracking data |
| title_sort | u net 3 for anomalous diffusion analysis enhanced with mixture estimates u and me in particle tracking data |
| topic | particle tracking machine learning microscopy data analysis anomalous diffusion |
| url | https://doi.org/10.1088/2515-7647/adf9aa |
| work_keys_str_mv | AT solomonasghar unet3foranomalousdiffusionanalysisenhancedwithmixtureestimatesuandmeinparticletrackingdata AT ranni unet3foranomalousdiffusionanalysisenhancedwithmixtureestimatesuandmeinparticletrackingdata AT giorgiovolpe unet3foranomalousdiffusionanalysisenhancedwithmixtureestimatesuandmeinparticletrackingdata |