Publications de Joowon Lim | EPFL Graph Search

Computational segmentation of nuclei and vacuoles based on statistical inference for tomographic phase microscopy in flow cytometry

Identifying intracellular organelles within the 3D label-free tomograms of cells’ refractive indexes recorded in flow cytometry is challenging. Here we present a method for the 3D statistical segmentation of nuclei and vacuoles in flowing cells. ...

Optical Society of America2024

MaxwellNet: Physics-driven deep neural network training based on Maxwell's equations

Demetri Psaltis, Joowon Lim

Maxwell's equations govern light propagation and its interaction with matter. Therefore, the solution of Maxwell's equations using computational electromagnetic simulations plays a critical role in understanding light-matter interaction and designing optic ...

AIP Publishing2022

Stain-free identification of cell nuclei using tomographic phase microscopy in flow cytometry

Demetri Psaltis, Joowon Lim, Daniele Pirone

The accurate identification of the three-dimensional quantitative shape of a cell nucleus is now possible without fluorescent staining by applying computational segmentation to refractive index tomograms recorded in the flow cytometry mode. ...

Nature Publishing Group2022

3D reconstruction of weakly scattering objects from 2D intensity-only measurements using the Wolf transform

Demetri Psaltis, Ahmed Ayoub, Joowon Lim, Elizabeth Elena Antoine

A new approach to optical diffraction tomography (ODT) based on intensity measurements is presented. By applying the Wolf transform directly to intensity measurements, we observed unexpected behavior in the 3D reconstruction of the sample. Such a reconstru ...

2021

Polarization-sensitive optics diffraction tomography

Demetri Psaltis, Ahmed Ayoub, Joowon Lim, Elizabeth Elena Antoine, Amirhossein Saba Shirvan

Polarization of light has been widely used as a contrast mechanism in two-dimensional (2D) microscopy and also in some three-dimensional (3D) imaging modalities. In this paper, we report the 3D tomographic reconstruction of the refractive index (RI) tensor ...

2021

Computer generated optical volume elements by additive manufacturing

Demetri Psaltis, Christophe Moser, Eirini Kakkava, Joowon Lim, Niyazi Ulas Dinç

Computer generated optical volume elements have been investigated for information storage, spectral filtering, and imaging applications. Advancements in additive manufacturing (3D printing) allow the fabrication of multilayered diffractive volume elements ...

2020

Three-Dimensional Optical Diffraction Tomography With Lippmann-Schwinger Model

Michaël Unser, Demetri Psaltis, Thanh-An Michel Pham, Ahmed Ayoub, Joowon Lim, Emmanuel Emilien Louis Soubies, Ahmed Bassam Sayed Ayoub Mohamed Emam

A broad class of imaging modalities involve the resolution of an inverse-scattering problem. Among them, three-dimensional optical diffraction tomography (ODT) comes with its own challenges. These include a limited range of views, a large size of the sampl ...

2020

Learning approaches to high-fidelity optical diffraction tomography

Joowon Lim

Optical diffraction tomography (ODT) provides us 3D refractive index (RI) distributions of transparent samples. Since RI values differ across different materials, they serve as endogenous contrasts. It, therefore, enables us to image without pre-processing ...

EPFL2020

A method for assessing the fidelity of optical diffraction tomography reconstruction methods using structured illumination

Michaël Unser, Demetri Psaltis, Thanh-An Michel Pham, Ahmed Ayoub, Joowon Lim

2020

Three-dimensional tomography of red blood cells using deep learning

Demetri Psaltis, Ahmed Ayoub, Joowon Lim

We accurately reconstruct three-dimensional (3-D) refractive index (RI) distributions from highly ill-posed two-dimensional (2-D) measurements using a deep neural network (DNN). Strong distortions are introduced on reconstructions obtained by the Wolf tran ...

2020

High-fidelity optical diffraction tomography of multiple scattering samples

Demetri Psaltis, Ahmed Ayoub, Joowon Lim, Elizabeth Elena Antoine

We propose an iterative reconstruction scheme for optical diffraction tomography that exploits the split-step nonparaxial (SSNP) method as the forward model in a learning tomography scheme. Compared with the beam propagation method (BPM) previously used in ...

Springer2019

Learning Tomography Assessed Using Mie Theory

Michaël Unser, Demetri Psaltis, Joowon Lim

In optical diffraction tomography, the multiply scattered field is a nonlinear function of the refractive index (RI) of the object. The Rytov method relies on a single-scattering propagation model and is commonly used to reconstruct images. Recently, a rec ...

American Physical Society (APS)2018

Versatile reconstruction framework for diffraction tomography with intensity measurements and multiple scattering

Michaël Unser, Demetri Psaltis, Thanh-An Michel Pham, Ferréol Arnaud Marie Soulez, Joowon Lim, Emmanuel Emilien Louis Soubies

2018

Imaging complex objects using learning tomography

Michaël Unser, Alexandre Goy, Demetri Psaltis, Morteza Hasani Shoreh, Joowon Lim

Optical diffraction tomography (ODT) can be described using the scattering process through an inhomogeneous media. An inherent nonlinearity exists relating the scattering medium and the scattered field due to multiple scattering. Multiple scattering is oft ...

SPIE-INT SOC OPTICAL ENGINEERING2018

Phaseless Diffraction Tomography With Regularized Beam Propagation

Michaël Unser, Alexandre Goy, Demetri Psaltis, Thanh-An Michel Pham, Ferréol Arnaud Marie Soulez, Joowon Lim, Emmanuel Emilien Louis Soubies

In recent years, researchers have obtained impressive reconstructions of the refractive index (RI) of biological objects through the combined use of advanced physics (nonlinear forward model) and regularization. Here, we propose an adaptation of these tech ...

IEEE2018

Optical Tomography Based On A Nonlinear Model That Handles Multiple Scattering

Michaël Unser, Alexandre Goy, Ulugbek Kamilov, Demetri Psaltis, Joowon Lim

Learning Tomography (LT) is a nonlinear optimization algorithm for computationally imaging three-dimensional (3D) distribution of the refractive index in semi-transparent samples. Since the energy function in LT is generally non-convex, the solution it obt ...

Ieee2017