Interview with Saga Helgadottir in Curie, a magazine issued by the Swedish Research Council

Saga Helgadottir discussed her research with Curie, a magazine issued by the Swedish Research Council. The article gives examples of how AI is used in many research disciplines. Read the article on Curie’s webpage here.

Presentation by Saga Helgadottir at the CECAM Workshop “Active Matter and Artificial Intelligence”, Lausanne, Switzerland, 30 September 2019

Digital video microscopy enhanced by deep learning

Saga Helgadottir, Aykut Argun & Giovanni Volpe
CECAM Workshop “Active Matter and Artificial Intelligence”, Lausanne, Switzerland
30 September 2019

Single particle tracking is essential in many branches of science and technology, from the measurement of biomolecular forces to the study of colloidal crystals. Standard methods rely on algorithmic approaches; by fine-tuning several user-defined parameters, these methods can be highly successful at tracking a well-defined kind of particle under low-noise conditions with constant and homogenous illumination. Here, we introduce an alternative data-driven approach based on a convolutional neural network, which we name DeepTrack. We show that DeepTrack outperforms algorithmic approaches, especially in the presence of noise and under poor illumination conditions. We use DeepTrack to track an optically trapped particle under very noisy and unsteady illumination conditions, where standard algorithmic approaches fail. We then demonstrate how DeepTrack can also be used to track multiple particles and non-spherical objects such as bacteria, also at very low signal-to-noise ratios. In order to make DeepTrack readily available for other users, we provide a Python software package, which can be easily personalized and optimized for specific applications.

Saga Helgadottir, Aykut Argun & Giovanni Volpe, Optica 6(4), 506—513 (2019)
doi: 10.1364/OPTICA.6.000506
arXiv: 1812.02653
GitHub: DeepTrack

03:40 PM–04:00 PM, Monday, September 30, 2019

Presentation by Saga Helgadottir at the AI for Health and Healthy AI conference, Gothenburg, Sweden, 30 August 2019

Digital video microscopy enhanced by deep learning

Saga Helgadottir, Aykut Argun & Giovanni Volpe
AI for Health and Healthy AI conference, Gothenburg, Sweden
30 August 2019

Single particle tracking is essential in many branches of science and technology, from the measurement of biomolecular forces to the study of colloidal crystals. Standard methods rely on algorithmic approaches; by fine-tuning several user-defined parameters, these methods can be highly successful at tracking a well-defined kind of particle under low-noise conditions with constant and homogenous illumination. Here, we introduce an alternative data-driven approach based on a convolutional neural network, which we name DeepTrack. We show that DeepTrack outperforms algorithmic approaches, especially in the presence of noise and under poor illumination conditions. We use DeepTrack to track an optically trapped particle under very noisy and unsteady illumination conditions, where standard algorithmic approaches fail. We then demonstrate how DeepTrack can also be used to track multiple particles and non-spherical objects such as bacteria, also at very low signal-to-noise ratios. In order to make DeepTrack readily available for other users, we provide a Python software package, which can be easily personalized and optimized for specific applications.

Friday, August 30, 2019

Saga Helgadottir, Aykut Argun & Giovanni Volpe, Optica 6(4), 506—513 (2019)
doi: 10.1364/OPTICA.6.000506
arXiv: 1812.02653
GitHub: DeepTrack

Invited Seminar by Saga Helgadottir at the Max Planck Institute for the Science of Light, 10 May 2019

Digital video microscopy enhanced by deep learning

Saga Helgadottir
Sandoghdar Division, Max Planck Institute for the Science of Light, Erlangen, Germany
10 May 2019

Single particle tracking is essential in many branches of science and technology, from the measurement of biomolecular forces to the study of colloidal crystals. Standard methods rely on algorithmic approaches; by fine-tuning several user-defined parameters, these methods can be highly successful at tracking a well-defined kind of particle under low-noise conditions with constant and homogenous illumination. Here, we introduce an alternative data-driven approach based on a convolutional neural network, which we name DeepTrack. We show that DeepTrack outperforms algorithmic approaches, especially in the presence of noise and under poor illumination conditions. We use DeepTrack to track an optically trapped particle under very noisy and unsteady illumination conditions, where standard algorithmic approaches fail. We then demonstrate how DeepTrack can also be used to track multiple particles and non-spherical objects such as bacteria, also at very low signal-to-noise ratios. In order to make DeepTrack readily available for other users, we provide a Python software package, which can be easily personalized and optimized for specific applications.

Saga Helgadottir, Aykut Argun & Giovanni Volpe, Optica 6(4), 506—513 (2019)
doi: 10.1364/OPTICA.6.000506
arXiv: 1812.02653
GitHub: DeepTrack

Presentation by Saga Helgadottir at the OSA Biophotonics Congress, Tucson, 16 Apr 2019

Digital video microscopy enhanced by deep learning

Saga Helgadottir, Aykut Argun & Giovanni Volpe
OSA Biophotonics Congress, Tucson (AZ), USA
16 April 2019

Single particle tracking is essential in many branches of science and technology, from the measurement of biomolecular forces to the study of colloidal crystals. Standard methods rely on algorithmic approaches; by fine-tuning several user-defined parameters, these methods can be highly successful at tracking a well-defined kind of particle under low-noise conditions with constant and homogenous illumination. Here, we introduce an alternative data-driven approach based on a convolutional neural network, which we name DeepTrack. We show that DeepTrack outperforms algorithmic approaches, especially in the presence of noise and under poor illumination conditions. We use DeepTrack to track an optically trapped particle under very noisy and unsteady illumination conditions, where standard algorithmic approaches fail. We then demonstrate how DeepTrack can also be used to track multiple particles and non-spherical objects such as bacteria, also at very low signal-to-noise ratios. In order to make DeepTrack readily available for other users, we provide a Python software package, which can be easily personalized and optimized for specific applications.

Session: Biological Applications
10:30 AM–12:00 AM, Tuesday, April 16, 2019

More information can be found on the link: https://www.osapublishing.org/abstract.cfm?uri=OMA-2019-AT2E.5