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Three-dimensional, quantitative imaging of biological cells and their internal structures performed by optical diffraction tomography (ODT) is an important part of biomedical research. However, conducting quantitative analysis of ODT images requires performing 3D segmentation with high accuracy, often unattainable with available segmentation methods. Therefore, in this work, we present a new semi-automatic method, called ODT-SAS, which combines several non-machine-learning techniques to segment cells and 2 types of their organelles: nucleoli and lipid structures (LS). ODT-SAS has been compared with Cellpose and slice-by-slice manual segmentation, respectively, in cell segmentation and organelles segmentation. The comparison shows superiority of ODT-SAS over Cellpose and reveals the potential of our technique in detecting cells, nucleoli and LS., Competing Interests: The authors declare no conflicts of interest., (© 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.)

Holographic tomography (HT) is a measurement technique that generates phase images, often containing high noise levels and irregularities. Due to the nature of phase retrieval algorithms within the HT data processing, the phase has to be unwrapped before tomographic reconstruction. Conventional algorithms lack noise robustness, reliability, speed, and possible automation. In order to address these problems, this work proposes a convolutional neural network based pipeline consisting of two steps: denoising and unwrapping. Both steps are carried out under the umbrella of a U-Net architecture; however, unwrapping is aided by introducing Attention Gates (AG) and Residual Blocks (RB) to the architecture. Through the experiments, the proposed pipeline makes possible the phase unwrapping of highly irregular, noisy, and complex experimental phase images captured in HT. This work proposes phase unwrapping carried out by segmentation with a U-Net network, that is aided by a pre-processing denoising step. It also discusses the implementation of the AGs and RBs in an ablation study. What is more, this is the first deep learning based solution that is trained solely on real images acquired with HT.

Yokukansan (YKS) is a traditional Japanese herbal medicine that is increasingly being studied for its effects on neurodegenerative diseases. In our study, we presented a novel methodology for a multimodal analysis of the effects of YKS on nerve cells. The measurements of 3D refractive index distribution and its changes performed by holographic tomography were supported with an investigation by Raman micro-spectroscopy and fluorescence microscopy to gather complementary morphological and chemical information about cells and YKS influence. It was shown that at the concentrations tested, YKS inhibits proliferation, possibly involving reactive oxygen species. Also substantial changes in the cell RI after few hours of YKS exposure were detected, followed by longer-term changes in cell lipid composition and chromatin state., Competing Interests: The authors declare that there are no conflicts of interest related to this article., (© 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.)

In this paper we present a structurally-complex biomimetic scattering structure, fabricated with two-photon polymerization, and utilize this object in order to benchmark a computational imaging system. The phantom allows to tailor the scattering by modifying its degrees of freedom i.e. refractive index contrast and scattering layer dimensions and incorporates a 3D imaging quality test, representing a single cell within tissue. While the sample may be used with multiple 3D microscopy techniques, we demonstrate the impact of scattering on three tomographic phase microscopy (TPM) reconstruction methods. One of these methods assumes the sample to be weak-scattering, while the other two take multiple scattering into account. The study is performed at two wavelengths (visible and near-infrared), which serve as a scaling factor for the scattering phenomenon. We find that changing the wavelength from visible into near-infrared impacts the applicability of TPM reconstruction methods. As a result of reduced scattering in near-infrared region, the multiple-scattering-oriented techniques perform in fact worse than a method aimed for weak-scattering samples. This implies a necessity of selecting proper approach depending on sample's scattering characteristics even in case of subtle changes in the object-light interaction., (© 2022. The Author(s).)

We present a holographic tomography technique in which the projections are acquired using both wavelength and illumination scanning in the near-infrared region. We show how to process the acquired data to obtain correct values of three-dimensional refractive index distributions in both single-wavelength and multi-wavelength data acquisition schemes and how to properly account for the dispersion of the sample. We perform numerical and experimental comparisons of different illumination scenarios to determine the most efficient measurement protocol. We show that the multi-wavelength protocol is advantageous in terms of signal-to-noise ratio and contrast-to-noise ratio over single-wavelength protocols, even for the same number of projections used for reconstructions. Finally, we show that this approach is suitable for providing high-quality refractive index distributions of relatively thick colon cancer samples., Competing Interests: The authors declare no conflicts of interest., (© 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.)

Tomographic quantitative phase imaging (QPI) lacks an absolute refractive index value baseline, which poses a problem when large dense objects extending over multiple fields of view are measured volume by volume and stitched together. Some of the measurements lack the natural baseline value that is provided by the mounting medium with a known refractive index. In this work, we discuss the problem of the refractive index (RI) baseline of individual reconstructed volumes that are deprived of access to mounting medium due to the extent of the object. The solution of this problem is provided by establishing the RI offsets based on the overlapping regions. We have proven that the process of finding the offset RI values may be justifiably reduced to the analogous procedure in the 2D baseline correction (2D-BC). Finally, we proposed the enhancement of the state-of-the-art 2D-BC procedure previously introduced in the context of 2D QPI. The processing is validated at the examples of a synthetic dataset and a liver organoid., Competing Interests: The authors declare that there are no conflicts of interest related to this article., (© 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.)

Low-level laser therapy (LLLT) is a therapeutic tool that uses the photobiochemical interaction between light and tissue. Its effectiveness is controversial due to a strong dependence on dosimetric parameters. In this work, we demonstrate that digital holographic microscopy is an effective label-free imaging technique to analyze the effects of LLLT on biological cells, and we propose the full methodology to create correct synthetic aperture phase maps for further extensive, highly accurate statistical analysis. The proposed methodology has been designed to provide a basis for many other biological experiments using quantitative phase imaging. We use SHSY-5Y and HaCaT cells irradiated with different doses of red light for the experiment. The analysis shows quantitative changes in cell dry mass density and the projected cell surface in response to different radiation doses.

Quantitative phase imaging of cell cultures and histopathological slides often requires measurements in large fields of view which is realized through the stitching of multiple high resolution phase maps. Due to the characteristic properties of phase images, careful preprocessing is crucial for maintaining the metrological value of the stitched phase image. In this work, we present various methods that address those properties. Our efforts are focused on increasing robustness to minimize error propagation in consecutive preprocessing steps., Competing Interests: The authors declare that there are no conflicts of interest related to this article., (© 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.)

Holographic tomography (HT) is an advanced label-free optical microscopic imaging method used for biological studies. HT uses digital holographic microscopy to record the complex amplitudes of a biological sample as digital holograms and then numerically reconstruct the sample's refractive index (RI) distribution in three dimensions. The RI values are a key parameter for label-free bio-examination, which correlate with metabolic activities and spatiotemporal distribution of biophysical parameters of cells and their internal organelles, tissues, and small-scale biological objects. This article provides insight on this rapidly growing HT field of research and its applications in biology. We present a review summary of the HT principle and highlight recent technical advancement in HT and its applications.

Limited-angle optical diffraction tomography suffers from strong artifacts in tomographic reconstructions. Numerous algorithms, mainly based on regularization methods, have been developed recently to overcome this limitation. However, the quality of results still needs further improvement. Here I present a simple yet extremely effective method of increasing the reconstruction quality in limited angle optical diffraction tomography that can be combined with known tomographic algorithms. In the method a finite object support is generated from the object data and utilized in the reconstruction procedure as an additional strong regularizer. Practical aspects of this method are given together with examples of application., Competing Interests: The authors declare that there are no conflicts of interest related to this article., (© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.)

In the paper we present a novel method of extended depth-of-field limited-angle optical diffraction tomography, in which the change of a focal plane position is performed with a liquid focus-tunable lens. One sinogram is acquired for each state of a focus-tunable lens. After acquisition process is complete, all sinograms are independently reconstructed and stitched to form the final tomographic reconstruction. The presented solution effectively extends the applicability of the Rytov approximation to relatively thick samples and provides uniform resolution of 3D tomographic reconstructions. The method is also combined with Generalized Total Variation Iterative Constraint algorithm, which minimizes distortion of the results due to the limited angular range of acquired projections. The combined solution is dedicated to investigation of transparent and semi-transparent biological micro-structures, like cells and tissue slices., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

Due to incompleteness of input data inherent to Limited Angle Tomography (LAT), specific additional constraints are usually employed to suppress image artifacts. In this work we demonstrate a new two-stage regularization strategy, named Generalized Total Variation Iterative Constraint (GTVIC), dedicated to semi-piecewise-constant objects. It has been successfully applied as a supplementary module for two different reconstruction algorithms: an X-ray type solver and a diffraction-wise solver. Numerical tests performed on a detailed phantom of a biological cell under conical illumination pattern show significant reduction of axial blurring in the reconstructed refractive index distribution after GTVIC is added. Analogous results were obtained with experimental data.

The use of light diffraction for the microbiological diagnosis of bacterial colonies was a significant breakthrough with widespread implications for the food industry and clinical practice. We previously confirmed that optical sensors for bacterial colony light diffraction can be used for bacterial identification. This paper is focused on the novel perspectives of this method based on digital in-line holography (DIH), which is able to reconstruct the amplitude and phase properties of examined objects, as well as the amplitude and phase patterns of the optical field scattered/diffracted by the bacterial colony in any chosen observation plane behind the object from single digital hologram. Analysis of the amplitude and phase patterns inside a colony revealed its unique optical properties, which are associated with the internal structure and geometry of the bacterial colony. Moreover, on a computational level, it is possible to select the desired scattered/diffracted pattern within the entire observation volume that exhibits the largest amount of unique, differentiating bacterial features. These properties distinguish this method from the already proposed sensing techniques based on light diffraction/scattering of bacterial colonies. The reconstructed diffraction patterns have a similar spatial distribution as the recorded Fresnel patterns, previously applied for bacterial identification with over 98% accuracy, but they are characterized by both intensity and phase distributions. Our results using digital holography provide new optical discriminators of bacterial species revealed in one single step in form of new optical signatures of bacterial colonies: digital holograms, reconstructed amplitude and phase patterns, as well as diffraction patterns from all observation space, which exhibit species-dependent features. To the best of our knowledge, this is the first report on bacterial colony analysis via digital holography and our study represents an innovative approach to the subject.

We demonstrate an active, holographic tomography system, working with limited angle of projections, realized by optical-only, diffraction-based beam steering. The system created for this purpose is a Mach–Zehnder interferometer modified to serve as a digital holographic microscope with a high numerical aperture illumination module and a spatial light modulator (SLM). Such a solution is fast and robust. Apart from providing an elegant solution to viewing angle shifting, it also adds new capabilities of the holographic microscope system. SLM, being an active optical element, allows wavefront correction in order to improve measurement accuracy. Integrated phase data captured with different illumination scenarios within a highly limited angular range are processed by a new tomographic reconstruction algorithm based on the compressed sensing technique: total variation minimization, which is applied here to reconstruct nonpiecewise constant samples. Finally, the accuracy of full measurement and the proposed processing path is tested for a calibrated three-dimensional micro-object as well as a biological object--C2C12 myoblast cell.