High-resolution transport-of-intensity quantitative phase microscopy with annular illumination

For quantitative phase imaging (QPI) based on transport-of-intensity equation (TIE), partially coherent illumination provides speckle-free imaging, compatibility with brightfield microscopy, and transverse resolution beyond coherent diffraction limit. Unfortunately, in a conventional microscope with circular illumination aperture, partial coherence tends to diminish the phase contrast, exacerbating the inherent noise-to-resolution tradeoff in TIE imaging, resulting in strong low-frequency artifacts and compromised imaging resolution. Here, we demonstrate how these issues can be effectively addressed by replacing the conventional circular illumination aperture with an annular one. The matched annular illumination not only strongly boosts the phase contrast for low spatial frequencies, but significantly improves the practical imaging resolution to near the incoherent diffraction limit. By incorporating high-numerical aperture (NA) illumination as well as high-NA objective, it is shown, for the first time, that TIE phase imaging can achieve a transverse resolution up to 208 nm, corresponding to an effective NA of 2.66. Time-lapse imaging of in vitro Hela cells revealing cellular morphology and subcellular dynamics during cells mitosis and apoptosis is exemplified. Given its capability for high-resolution QPI as well as the compatibility with widely available brightfield microscopy hardware, the proposed approach is expected to be adopted by the wider biology and medicine community.Comment: This manuscript was originally submitted on 20 Feb. 201

Nucleus segmentation : towards automated solutions

Single nucleus segmentation is a frequent challenge of microscopy image processing, since it is the first step of many quantitative data analysis pipelines. The quality of tracking single cells, extracting features or classifying cellular phenotypes strongly depends on segmentation accuracy. Worldwide competitions have been held, aiming to improve segmentation, and recent years have definitely brought significant improvements: large annotated datasets are now freely available, several 2D segmentation strategies have been extended to 3D, and deep learning approaches have increased accuracy. However, even today, no generally accepted solution and benchmarking platform exist. We review the most recent single-cell segmentation tools, and provide an interactive method browser to select the most appropriate solution.Peer reviewe

Convolutional Neural Networks for Cellular Segmentation

Üha enam lülituvad algoritmid töö tegemisel väärtuslikeks abimeesteks. Tänapäevase tehnoloogia toel on võimalik inimesed vabastada lihtsamatest ülesannetest, et nad saaksid keskenduda teistele töödele, mis on arvuti jaoks keerulised. Üks abistavatest tehnoloogiatest on süvaõpe. Selle abil suudavad arvutid lahendada ülesandeid, mida varem peeti arvutite jaoks raskeks või koguni võimatuks.Üheks selliseks tööks on erevälja rakupiltide segmenteerimine. Seda on tarvis eelkõige biomeditsiinilaborites ning ravimifirmades, mis peavad suurt hulka mikroskoobipilte analüüsima ja kvantifitseerima. Praegused tööprotsessid väldivad ereväljapiltide kasutust, kuna nende segmenteerimiseks pole tööstuslikke lahendusi ning käsitsi töötlemine on keerukas ja aeganõudev.Magistritöö eesmärgiks on tõestada, et masinõpe suudab lahendada seni masinatele raskete ereväljapiltide segmenteerimise ülesande. Loodud lahendus aitab teadlastel üle maailma katsetada teisi uurimismeetodeid ja säästa palju aega.There is a persistent demand for work-assisting algorithms in industry. Using present-day technology, it is possible to free people from mundane tasks so they can concentrate on work that requires human skills and flexibility. Deep learning methods can complete tasks that were previously considered hard or even impossible for machines.One example of this kind of task is segmenting brightfield microscopy images of cells. This work is needed mostly in biomedical laboratories and pharmaceutical companies that must analyse and quantify vast amounts of image data. Current workflows avoid useful brightfield imagery because automatic industrial solutions for segmentation do not exist. Manual annotation is very challenging and time consuming, even for experienced professionals.The goal of the thesis is to demonstrate that deep learning can solve the task of segmenting challenging brightfield images. The developed solution opens new experimental approaches, saving time and resources for biomedical scientists across the globe

Low-cost portable microscopy systems for biomedical imaging and healthcare applications

In recent years, the development of low-cost portable microscopes (LPMs) has opened new possibilities for disease detection and biomedical research, especially in resource-limited areas. Despite these advancements, the majority of existing LPMs are hampered by sophisticated optical and mechanical designs, require extensive post-data analysis, and are often tailored for specific biomedical applications, limiting their broader utility. Furthermore, creating an optical-sectioning microscope that is both compact and cost effective presents a significant challenge. Addressing these critical gaps, this PhD study aims to: (1) develop a universally applicable LPM featuring a simplified mechanical and optical design for real-time biomedical imaging analysis, and (2) design a novel, smartphone-based optical sectioning microscope that is both compact and affordable. These objectives are driven by the need to enhance accessibility to quality diagnostic tools in varied settings, promising a significant leap forward in the democratization of biomedical imaging technologies. With 3D printing, optimised optical design, and AI techniques, we can develop LPM’s real time analysis functionality. I conducted a literature review on LPMs and related applications in my study and implemented two low-cost prototype microscopes and one theoretical study. 1) The first project is a portable AI fluorescence microscope based on a webcam and the NVIDIA Jetson Nano (NJN) with real-time analysis functionality. The system was 3D printed, weighing ~250 grams with a size of 145mm × 172 mm × 144 mm (L×W×H) and costing ~$400. It achieves a physical magnification of ×5 and can resolve 228.1 lp/mm USAF features. The system can recognise and count fluorescent beads and human red blood cells (RBCs). 2) I developed a smartphone-based optical sectioning microscope using the HiLo technique. To our knowledge, it is the first smartphone-based HiLo microscope that offers low-cost optical-sectioned widefield imaging. It has a 571.5 μm telecentric scanning range and an 11.7 μm axial resolution. I successfully used it to realize optical sectioning imaging of fluorescent beads. For this system, I developed a new low-cost HiLo microscopy technique using microlens arrays (MLAs) with incoherent light-emitting diode (LED) light sources. I conducted a numerical simulation study assessing the integration of uncoherent LEDs and MLAs for a low-cost HiLo system. The MLA can generate structured illumination in HiLo. How the MLA’s geometry structure and physical parameters affect the image performance were discussed in detail. This PhD thesis explores the advancement of low-cost portable microscopes (LPMs) through the integration of 3D printing, optimized optical design, and artificial intelligence (AI) techniques to enhance their real-time analysis capabilities. The research involved a comprehensive literature review on LPMs and their applications, leading to the development of two innovative prototype LPMs, alongside a theoretical study. These works contribute significantly to the field by not only addressing the technical and financial barriers associated with advanced microscopy but also by laying the groundwork for future innovations in portable and accessible biomedical imaging. Through its focus on simplification, affordability, and practicality, the research holds promise for substantially expanding the reach and impact of diagnostic imaging technologies, especially in those resource-limited areas.In recent years, the development of low-cost portable microscopes (LPMs) has opened new possibilities for disease detection and biomedical research, especially in resource-limited areas. Despite these advancements, the majority of existing LPMs are hampered by sophisticated optical and mechanical designs, require extensive post-data analysis, and are often tailored for specific biomedical applications, limiting their broader utility. Furthermore, creating an optical-sectioning microscope that is both compact and cost effective presents a significant challenge. Addressing these critical gaps, this PhD study aims to: (1) develop a universally applicable LPM featuring a simplified mechanical and optical design for real-time biomedical imaging analysis, and (2) design a novel, smartphone-based optical sectioning microscope that is both compact and affordable. These objectives are driven by the need to enhance accessibility to quality diagnostic tools in varied settings, promising a significant leap forward in the democratization of biomedical imaging technologies. With 3D printing, optimised optical design, and AI techniques, we can develop LPM’s real time analysis functionality. I conducted a literature review on LPMs and related applications in my study and implemented two low-cost prototype microscopes and one theoretical study. 1) The first project is a portable AI fluorescence microscope based on a webcam and the NVIDIA Jetson Nano (NJN) with real-time analysis functionality. The system was 3D printed, weighing ~250 grams with a size of 145mm × 172 mm × 144 mm (L×W×H) and costing ~$400. It achieves a physical magnification of ×5 and can resolve 228.1 lp/mm USAF features. The system can recognise and count fluorescent beads and human red blood cells (RBCs). 2) I developed a smartphone-based optical sectioning microscope using the HiLo technique. To our knowledge, it is the first smartphone-based HiLo microscope that offers low-cost optical-sectioned widefield imaging. It has a 571.5 μm telecentric scanning range and an 11.7 μm axial resolution. I successfully used it to realize optical sectioning imaging of fluorescent beads. For this system, I developed a new low-cost HiLo microscopy technique using microlens arrays (MLAs) with incoherent light-emitting diode (LED) light sources. I conducted a numerical simulation study assessing the integration of uncoherent LEDs and MLAs for a low-cost HiLo system. The MLA can generate structured illumination in HiLo. How the MLA’s geometry structure and physical parameters affect the image performance were discussed in detail. This PhD thesis explores the advancement of low-cost portable microscopes (LPMs) through the integration of 3D printing, optimized optical design, and artificial intelligence (AI) techniques to enhance their real-time analysis capabilities. The research involved a comprehensive literature review on LPMs and their applications, leading to the development of two innovative prototype LPMs, alongside a theoretical study. These works contribute significantly to the field by not only addressing the technical and financial barriers associated with advanced microscopy but also by laying the groundwork for future innovations in portable and accessible biomedical imaging. Through its focus on simplification, affordability, and practicality, the research holds promise for substantially expanding the reach and impact of diagnostic imaging technologies, especially in those resource-limited areas

ColiCoords: A Python package for the analysis of bacterial fluorescence microscopy data

Single-molecule fluorescence microscopy studies of bacteria provide unique insights into the mechanisms of cellular processes and protein machineries in ways that are unrivalled by any other technique. With the cost of microscopes dropping and the availability of fully automated microscopes, the volume of microscopy data produced has increased tremendously. These developments have moved the bottleneck of throughput from image acquisition and sample preparation to data analysis. Furthermore, requirements for analysis procedures have become more stringent given the demand of various journals to make data and analysis procedures available. To address these issues we have developed a new data analysis package for analysis of fluorescence microscopy data from rod-like cells. Our software ColiCoords structures microscopy data at the single-cell level and implements a coordinate system describing each cell. This allows for the transformation of Cartesian coordinates from transmission light and fluorescence images and single-molecule localization microscopy (SMLM) data to cellular coordinates. Using this transformation, many cells can be combined to increase the statistical power of fluorescence microscopy datasets of any kind. ColiCoords is open source, implemented in the programming language Python, and is extensively documented. This allows for modifications for specific needs or to inspect and publish data analysis procedures. By providing a format that allows for easy sharing of code and associated data, we intend to promote open and reproducible research. The source code and documentation can be found via the project’s GitHub page

SpheroidPicker for automated 3D cell culture manipulation using deep learning

Recent statistics report that more than 3.7 million new cases of cancer occur in Europe yearly, and the disease accounts for approximately 20% of all deaths. High-throughput screening of cancer cell cultures has dominated the search for novel, effective anticancer therapies in the past decades. Recently, functional assays with patient-derived ex vivo 3D cell culture have gained importance for drug discovery and precision medicine. We recently evaluated the major advancements and needs for the 3D cell culture screening, and concluded that strictly standardized and robust sample preparation is the most desired development. Here we propose an artificial intelligence-guided low-cost 3D cell culture delivery system. It consists of a light microscope, a micromanipulator, a syringe pump, and a controller computer. The system performs morphology-based feature analysis on spheroids and can select uniform sized or shaped spheroids to transfer them between various sample holders. It can select the samples from standard sample holders, including Petri dishes and microwell plates, and then transfer them to a variety of holders up to 384 well plates. The device performs reliable semi- and fully automated spheroid transfer. This results in highly controlled experimental conditions and eliminates non-trivial side effects of sample variability that is a key aspect towards next-generation precision medicine.Peer reviewe

AI-driven projection tomography with multicore fibre-optic cell rotation

Optical tomography has emerged as a non-invasive imaging method, providing three-dimensional insights into subcellular structures and thereby enabling a deeper understanding of cellular functions, interactions, and processes. Conventional optical tomography methods are constrained by a limited illumination scanning range, leading to anisotropic resolution and incomplete imaging of cellular structures. To overcome this problem, we employ a compact multi-core fibre-optic cell rotator system that facilitates precise optical manipulation of cells within a microfluidic chip, achieving full-angle projection tomography with isotropic resolution. Moreover, we demonstrate an AI-driven tomographic reconstruction workflow, which can be a paradigm shift from conventional computational methods, often demanding manual processing, to a fully autonomous process. The performance of the proposed cell rotation tomography approach is validated through the three-dimensional reconstruction of cell phantoms and HL60 human cancer cells. The versatility of this learning-based tomographic reconstruction workflow paves the way for its broad application across diverse tomographic imaging modalities, including but not limited to flow cytometry tomography and acoustic rotation tomography. Therefore, this AI-driven approach can propel advancements in cell biology, aiding in the inception of pioneering therapeutics, and augmenting early-stage cancer diagnostics.Comment: 15 pages, 6 figure

A Weakly Supervised Deep Learning Approach for Detecting Malaria and Sickle Cells in Blood Films

Machine vision analysis of blood films imaged under a brightfield microscope could provide scalable malaria diagnosis solutions in resource constrained endemic urban settings. The major bottleneck in successfully analyzing blood films with deep learning vision techniques is a lack of object-level annotations of disease markers such as parasites or abnormal red blood cells. To overcome this challenge, this work proposes a novel deep learning supervised approach that leverages weak labels readily available from routine clinical microscopy to diagnose malaria in thick blood film microscopy. This approach is based on aggregating the convolutional features of multiple objects present in one hundred high resolution image fields. We show that this method not only achieves expert-level malaria diagnostic accuracy without any hard object-level labels but can also identify individual malaria parasites in digitized thick blood films, which is useful in assessing disease severity and response to treatment. We demonstrate another application scenario where our approach is able to detect sickle cells in thin blood films. We discuss the wider applicability of the approach in automated analysis of thick blood films for the diagnosis of other blood disorders