Publications by Michael Kellman
2022
Ruiming Cao; Michael Kellman; David Ren; Regina Eckert; Laura Waller
Self-calibrated 3D differential phase contrast microscopy with optimized illumination Journal Article
In: Biomed. Opt. Express, vol. 13, no. 3, pp. 1671–1684, 2022.
@article{Cao:22,
title = {Self-calibrated 3D differential phase contrast microscopy with optimized illumination},
author = {Ruiming Cao and Michael Kellman and David Ren and Regina Eckert and Laura Waller},
url = {http://opg.optica.org/boe/abstract.cfm?URI=boe-13-3-1671},
doi = {10.1364/BOE.450838},
year = {2022},
date = {2022-03-01},
urldate = {2022-03-01},
journal = {Biomed. Opt. Express},
volume = {13},
number = {3},
pages = {1671--1684},
publisher = {OSA},
abstract = {3D phase imaging recovers an object’s volumetric refractive index from intensity and/or holographic measurements. Partially coherent methods, such as illumination-based differential phase contrast (DPC), are particularly simple to implement in a commercial brightfield microscope. 3D DPC acquires images at multiple focus positions and with different illumination source patterns in order to reconstruct 3D refractive index. Here, we present a practical extension of the 3D DPC method that does not require a precise motion stage for scanning the focus and uses optimized illumination patterns for improved performance. The user scans the focus by hand, using the microscope’s focus knob, and the algorithm self-calibrates the axial position to solve for the 3D refractive index of the sample through a computational inverse problem. We further show that the illumination patterns can be optimized by an end-to-end learning procedure. Combining these two, we demonstrate improved 3D DPC with a commercial microscope whose only hardware modification is LED array illumination.},
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3D phase imaging recovers an object’s volumetric refractive index from intensity and/or holographic measurements. Partially coherent methods, such as illumination-based differential phase contrast (DPC), are particularly simple to implement in a commercial brightfield microscope. 3D DPC acquires images at multiple focus positions and with different illumination source patterns in order to reconstruct 3D refractive index. Here, we present a practical extension of the 3D DPC method that does not require a precise motion stage for scanning the focus and uses optimized illumination patterns for improved performance. The user scans the focus by hand, using the microscope’s focus knob, and the algorithm self-calibrates the axial position to solve for the 3D refractive index of the sample through a computational inverse problem. We further show that the illumination patterns can be optimized by an end-to-end learning procedure. Combining these two, we demonstrate improved 3D DPC with a commercial microscope whose only hardware modification is LED array illumination.
2021
Ruiming Cao; Michael Kellman; David Yonghuan Ren; Regina Eckert; Laura Waller
Algorithmic self-calibration for optimized 3D quantitative differential phase contrast microscopy Inproceedings
In: Quantitative Phase Imaging VII, pp. 116530R, International Society for Optics and Photonics 2021.
@inproceedings{cao2021algorithmic,
title = {Algorithmic self-calibration for optimized 3D quantitative differential phase contrast microscopy},
author = {Ruiming Cao and Michael Kellman and David Yonghuan Ren and Regina Eckert and Laura Waller},
url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11653/116530R/Algorithmic-self-calibration-for-optimized-3D-quantitative-differential-phase-contrast/10.1117/12.2577318.short?SSO=1},
year = {2021},
date = {2021-01-01},
booktitle = {Quantitative Phase Imaging VII},
volume = {11653},
pages = {116530R},
organization = {International Society for Optics and Photonics},
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pubstate = {published},
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2020
Michael Kellman; Kevin Zhang; Eric Markley; Jon Tamir; Emrah Bostan; Michael Lustig; Laura Waller
Memory-efficient Learning for Large-Scale Computational Imaging Journal Article
In: IEEE Transactions on Computational Imaging, vol. 6, pp. 1403-1414, 2020.
@article{kellman2020memory,
title = {Memory-efficient Learning for Large-Scale Computational Imaging},
author = { Michael Kellman and Kevin Zhang and Eric Markley and Jon Tamir and Emrah Bostan and Michael Lustig and Laura Waller},
url = {https://ieeexplore.ieee.org/document/9204455},
doi = {10.1109/TCI.2020.3025735},
year = {2020},
date = {2020-10-14},
journal = {IEEE Transactions on Computational Imaging},
volume = {6},
pages = {1403-1414},
keywords = {},
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Michael Kellman
Physics-based Learning for Large-scale Computational Imaging PhD Thesis
EECS Department, University of California, Berkeley, 2020.
@phdthesis{Kellman:EECS-2020-167,
title = {Physics-based Learning for Large-scale Computational Imaging},
author = {Michael Kellman},
url = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-167.html},
year = {2020},
date = {2020-08-01},
number = {UCB/EECS-2020-167},
school = {EECS Department, University of California, Berkeley},
abstract = {In computational imaging systems (e.g. tomographic systems, computational optics, magnetic resonance imaging) the acquisition of data and reconstruction of images are co-designed to retrieve information which is not traditionally accessible. The performance of such systems is characterized by how information is encoded to (forward process) and decoded from (inverse problem) the measurements. Recently, critical aspects of these systems, such as their signal prior, have been optimized using deep neural networks formed from unrolling the iterations of a physics-based image reconstruction.
In this dissertation, I will detail my work, physics-based learned design, to optimize the performance of the entire computational imaging system by jointly learning aspects of its experimental design and computational reconstruction. As an application, I introduce how the LED-array microscope performs super-resolved quantitative phase imaging and demonstrate how physics-based learning can optimize a reduced set of measurements without sacrificing performance to enable the imaging of live fast moving biology.
In this dissertation's latter half, I will discuss how to overcome some of the computational challenges encountered in applying physics-based learning concepts to large-scale computational imaging systems. I will describe my work, memory-efficient learning, that makes physics-based learning for large-scale systems feasible on commercially-available graphics processing units. I demonstrate this method on two large-scale real-world systems: 3D multi-channel compressed sensing MRI and super-resolution optical microscopy.},
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pubstate = {published},
tppubtype = {phdthesis}
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In computational imaging systems (e.g. tomographic systems, computational optics, magnetic resonance imaging) the acquisition of data and reconstruction of images are co-designed to retrieve information which is not traditionally accessible. The performance of such systems is characterized by how information is encoded to (forward process) and decoded from (inverse problem) the measurements. Recently, critical aspects of these systems, such as their signal prior, have been optimized using deep neural networks formed from unrolling the iterations of a physics-based image reconstruction.
In this dissertation, I will detail my work, physics-based learned design, to optimize the performance of the entire computational imaging system by jointly learning aspects of its experimental design and computational reconstruction. As an application, I introduce how the LED-array microscope performs super-resolved quantitative phase imaging and demonstrate how physics-based learning can optimize a reduced set of measurements without sacrificing performance to enable the imaging of live fast moving biology.
In this dissertation's latter half, I will discuss how to overcome some of the computational challenges encountered in applying physics-based learning concepts to large-scale computational imaging systems. I will describe my work, memory-efficient learning, that makes physics-based learning for large-scale systems feasible on commercially-available graphics processing units. I demonstrate this method on two large-scale real-world systems: 3D multi-channel compressed sensing MRI and super-resolution optical microscopy.
In this dissertation, I will detail my work, physics-based learned design, to optimize the performance of the entire computational imaging system by jointly learning aspects of its experimental design and computational reconstruction. As an application, I introduce how the LED-array microscope performs super-resolved quantitative phase imaging and demonstrate how physics-based learning can optimize a reduced set of measurements without sacrificing performance to enable the imaging of live fast moving biology.
In this dissertation's latter half, I will discuss how to overcome some of the computational challenges encountered in applying physics-based learning concepts to large-scale computational imaging systems. I will describe my work, memory-efficient learning, that makes physics-based learning for large-scale systems feasible on commercially-available graphics processing units. I demonstrate this method on two large-scale real-world systems: 3D multi-channel compressed sensing MRI and super-resolution optical microscopy.
Emrah Bostan; Reinhard Heckel; Michael Chen; Michael Kellman; Laura Waller
Deep Phase Decoder: Self-calibrating phase microscopy with an untrained deep neural network Journal Article
In: Optica, vol. 7, no. 6, pp. 559-562, 2020.
@article{bostan2020deep,
title = {Deep Phase Decoder: Self-calibrating phase microscopy with an untrained deep neural network},
author = { Emrah Bostan and Reinhard Heckel and Michael Chen and Michael Kellman and Laura Waller},
url = {https://doi.org/10.1364/OPTICA.389314
https://arxiv.org/abs/2001.09803},
doi = {10.1364/OPTICA.389314},
year = {2020},
date = {2020-05-21},
journal = {Optica},
volume = {7},
number = {6},
pages = {559-562},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Regina Eckert; Michael Kellman; Laura Waller
Physics-based learning for measurement diversity in 3D refractive index microscopy Inproceedings
In: Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVII, pp. 112450X, International Society for Optics and Photonics 2020.
@inproceedings{eckert2020physics,
title = {Physics-based learning for measurement diversity in 3D refractive index microscopy},
author = { Regina Eckert and Michael Kellman and Laura Waller},
url = {https://doi.org/10.1117/12.2543402},
year = {2020},
date = {2020-03-09},
booktitle = {Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVII},
volume = {11245},
pages = {112450X},
organization = {International Society for Optics and Photonics},
keywords = {},
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Michael Kellman; Emrah Bostan; Michael Lustig; Laura Waller
Data-driven experimental design for computational imaging Inproceedings
In: Quantitative Phase Imaging VI, pp. 1124916, International Society for Optics and Photonics 2020.
@inproceedings{kellman2020data,
title = {Data-driven experimental design for computational imaging},
author = { Michael Kellman and Emrah Bostan and Michael Lustig and Laura Waller},
year = {2020},
date = {2020-02-03},
booktitle = {Quantitative Phase Imaging VI},
volume = {11249},
pages = {1124916},
organization = {International Society for Optics and Photonics},
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Kevin Zhang; Michael Kellman; Emrah Bostan; Laura Waller
3D fluorescence deconvolution with deep priors Inproceedings
In: Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVII, pp. 112450N, International Society for Optics and Photonics 2020.
@inproceedings{zhang20203d,
title = {3D fluorescence deconvolution with deep priors},
author = { Kevin Zhang and Michael Kellman and Emrah Bostan and Laura Waller},
year = {2020},
date = {2020-02-03},
booktitle = {Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVII},
volume = {11245},
pages = {112450N},
organization = {International Society for Optics and Photonics},
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Ruiming Cao; Michael Kellman; David Ren; Laura Waller
3D Differential Phase Contrast Microscopy with Axial Motion Deblurring Inproceedings
In: Imaging and Applied Optics Congress, pp. CF4C.2, Optical Society of America, 2020.
@inproceedings{Cao:20,
title = {3D Differential Phase Contrast Microscopy with Axial Motion Deblurring},
author = {Ruiming Cao and Michael Kellman and David Ren and Laura Waller},
url = {http://www.osapublishing.org/abstract.cfm?URI=COSI-2020-CF4C.2},
year = {2020},
date = {2020-01-01},
booktitle = {Imaging and Applied Optics Congress},
journal = {Imaging and Applied Optics Congress},
pages = {CF4C.2},
publisher = {Optical Society of America},
abstract = {We demonstrate 3D phase imaging using asymmetric illumination patterns and defocused intensity measurements taken with continuous axial motion. The sample's 3D refractive index is reconstructed with a motion-corrected transfer function.},
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pubstate = {published},
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We demonstrate 3D phase imaging using asymmetric illumination patterns and defocused intensity measurements taken with continuous axial motion. The sample's 3D refractive index is reconstructed with a motion-corrected transfer function.
Ruiming Cao; Michael Kellman; David Ren; Laura Waller
3D Differential Phase Contrast Microscopy with Axial Motion Deblurring Inproceedings
In: Imaging and Applied Optics Congress, pp. CF4C.2, Optical Society of America, 2020.
@inproceedings{Cao:20b,
title = {3D Differential Phase Contrast Microscopy with Axial Motion Deblurring},
author = {Ruiming Cao and Michael Kellman and David Ren and Laura Waller},
url = {http://www.osapublishing.org/abstract.cfm?URI=COSI-2020-CF4C.2},
year = {2020},
date = {2020-01-01},
booktitle = {Imaging and Applied Optics Congress},
journal = {Imaging and Applied Optics Congress},
pages = {CF4C.2},
publisher = {Optical Society of America},
abstract = {We demonstrate 3D phase imaging using asymmetric illumination patterns and defocused intensity measurements taken with continuous axial motion. The sample's 3D refractive index is reconstructed with a motion-corrected transfer function.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We demonstrate 3D phase imaging using asymmetric illumination patterns and defocused intensity measurements taken with continuous axial motion. The sample's 3D refractive index is reconstructed with a motion-corrected transfer function.
2019
Matthew Wells; Jesus Deloya Garcia; Shwetadwip Chowdhury; Michael Kellman; Laura Waller; Rachel Pepper
Characterizing the feeding current of sessile microorganisms using digital holography Inproceedings
In: APS Division of Fluid Dynamics , 2019.
@inproceedings{wells2019characterizing,
title = {Characterizing the feeding current of sessile microorganisms using digital holography},
author = { Matthew Wells and Jesus Deloya Garcia and Shwetadwip Chowdhury and Michael Kellman and Laura Waller and Rachel Pepper},
url = {https://ui.adsabs.harvard.edu/abs/2019APS..DFDN05082W/abstract},
year = {2019},
date = {2019-11-01},
booktitle = {APS Division of Fluid Dynamics },
journal = {APS},
volume = {Fall 2019},
number = {NP05--082},
abstract = {Microscopic sessile suspension feeders (MSSFs) are single-celled organisms that live attached to surfaces in freshwater and marine environments. MSSFs generate a feeding current to consume bacteria and debris. They play a vital role in aquatic ecosystems of moving carbon up the food chain. Therefore, characterizing MSSF feeding flow is important for understanding their role in the environment. MSSF feeding flows have only been viewed in 2D and often with samples pressed between two coverslips that distort the flow. However, calculations predict the feeding current forms recirculating eddies when the MSSF body is oriented perpendicular to the surface of attachment. These eddies reduce feeding rate, and as the MSSF tends toward a parallel orientation it is predicted that the eddies disappear. Here we test those predictions by measuring 3D flow around individual Vorticella. Vorticellaare a common and representative MSSF. We use digital holography to record 3D videos of Vorticella in water seeded with 5-10 μm spherical flow tracers. We then use Particle Tracking Velocimetry to find fluid velocity from particle trajectories. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Microscopic sessile suspension feeders (MSSFs) are single-celled organisms that live attached to surfaces in freshwater and marine environments. MSSFs generate a feeding current to consume bacteria and debris. They play a vital role in aquatic ecosystems of moving carbon up the food chain. Therefore, characterizing MSSF feeding flow is important for understanding their role in the environment. MSSF feeding flows have only been viewed in 2D and often with samples pressed between two coverslips that distort the flow. However, calculations predict the feeding current forms recirculating eddies when the MSSF body is oriented perpendicular to the surface of attachment. These eddies reduce feeding rate, and as the MSSF tends toward a parallel orientation it is predicted that the eddies disappear. Here we test those predictions by measuring 3D flow around individual Vorticella. Vorticellaare a common and representative MSSF. We use digital holography to record 3D videos of Vorticella in water seeded with 5-10 μm spherical flow tracers. We then use Particle Tracking Velocimetry to find fluid velocity from particle trajectories.
Michael Kellman; Emrah Bostan; Nicole A Repina; Laura Waller
Physics-based learned design: Optimized coded-illumination for quantitative phase imaging Journal Article
In: IEEE Transactions on Computational Imaging, vol. 5, no. 3, pp. 344–353, 2019.
@article{kellman2019physics,
title = {Physics-based learned design: Optimized coded-illumination for quantitative phase imaging},
author = { Michael Kellman and Emrah Bostan and Nicole A Repina and Laura Waller},
url = {https://ieeexplore.ieee.org/document/8667888},
year = {2019},
date = {2019-09-01},
journal = {IEEE Transactions on Computational Imaging},
volume = {5},
number = {3},
pages = {344--353},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Michael Kellman; Emrah Bostan; Michael Chen; Laura Waller
Data-driven design for Fourier ptychographic microscopy Inproceedings
In: 2019 IEEE International Conference on Computational Photography (ICCP), pp. 1–8, IEEE 2019.
@inproceedings{kellman2019data,
title = {Data-driven design for Fourier ptychographic microscopy},
author = { Michael Kellman and Emrah Bostan and Michael Chen and Laura Waller},
url = {https://ieeexplore.ieee.org/abstract/document/8747339},
year = {2019},
date = {2019-05-15},
booktitle = {2019 IEEE International Conference on Computational Photography (ICCP)},
pages = {1--8},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2018
Michael Kellman; Michael Chen; Zachary F Phillips; Michael Lustig; Laura Waller
Motion-resolved quantitative phase imaging Journal Article
In: Biomedical optics express, vol. 9, no. 11, pp. 5456–5466, 2018.
@article{kellman2018motionb,
title = {Motion-resolved quantitative phase imaging},
author = { Michael Kellman and Michael Chen and Zachary F Phillips and Michael Lustig and Laura Waller},
url = {https://doi.org/10.1364/BOE.9.005456},
doi = {10.1364/BOE.9.005456},
year = {2018},
date = {2018-10-15},
journal = {Biomedical optics express},
volume = {9},
number = {11},
pages = {5456--5466},
publisher = {Optical Society of America},
keywords = {},
pubstate = {published},
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Michael Kellman; Zachary F Phillips; David Ren; Michael Lustig; Laura Waller
Motion resolved quantitative phase imaging Inproceedings
In: Computational Imaging III, pp. 106690D, International Society for Optics and Photonics 2018.
@inproceedings{kellman2018motion,
title = {Motion resolved quantitative phase imaging},
author = { Michael Kellman and Zachary F Phillips and David Ren and Michael Lustig and Laura Waller},
url = {https://doi.org/10.1117/12.2300100},
doi = {10.1117/12.2300100},
year = {2018},
date = {2018-05-15},
booktitle = {Computational Imaging III},
volume = {10669},
pages = {106690D},
organization = {International Society for Optics and Photonics},
keywords = {},
pubstate = {published},
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}