3D Microscopy Images Research Articles

DIRT/µ - Automated extraction of root hair traits using combinatorial optimization.

Similar to any microscopic appendages, such as cilia or antennae, phenotyping of root hairs has been a challenge due to their complex intersecting arrangements in two-dimensional (2D) images and the technical limitations of automated measurements. Digital Imaging of Root Traits at Microscale (DIRT/μ) addresses this issue by computationally resolving intersections and extracting individual root hairs from 2D microscopy images. This solution enables automatic and precise trait measurements of individual root hairs. DIRT/μ rigorously defines a set of rules to resolve intersecting root hairs and minimizes a newly designed cost function to combinatorically identify each root hair in the microscopy image. As a result, DIRT/μ accurately measures traits such as root hair length (RHL) distribution and root hair density (RHD), which are impractical for manual assessment. We tested DIRT/μ on three datasets to validate its performance and showcase potential applications. By measuring root hair traits in a fraction of the time manual methods require, DIRT/μ eliminates subjective biases from manual measurements. Automating individual root hair extraction accelerates phenotyping and quantifies trait variability within and among plants, creating new possibilities to characterize root hair function and their underlying genetics.

DATA DRIVEN AND CELL SPECIFIC DETERMINATION OF NUCLEI-ASSOCIATED ACTIN STRUCTURE

In the specialized field of cellular mechanosignaling, the intricate relationship between the cytoskeleton and nucleus is crucial for determining the behavior and fate of mesenchymal stem cells (MSCs). Existing analytical methods for filamentous actin fibers (F-actin) suffer from issues of accuracy and reproducibility. To address this, our research introduces a significant advancement in image analysis by utilizing two deep-learning segmentation Convolutional Neural Networks (CNNs) based on U-Net architecture. This algorithm precisely quantifies F-actin and offers reproducible data from 3D confocal microscopy images. Our CNNs automatically extract the 3D shape of both the nucleus and individual actin fibers, thereby enabling the geometric reconstruction of these critical cellular components. We applied this methodology to MSCs exposed to low-intensity vibration, and hypothesized that mechanical stimuli will induce perinuclear actin remodeling. Confocal Z-stack images of MSCs were captured using a 40x oil lens and a Zeiss LSM 900 microscope. Nucleus and F-actin structures were stained with Hoechst 33342 and Phalloidin, respectively. Nucleus-based images were employed to mask and isolate actin fibers within the nuclear vicinity. A specialized deep-learning segmentation model was engineered within the PyTorch framework. For model training, a selective 2% of nuclei and F-actin images were annotated. Specifically, three layers from each of six representative nuclei, among approximately 500 cross-sectional layers, were meticulously selected; a similar approach was employed for actin. The model underwent 200 epochs of training. Post-training, the deep-learning model was employed to process all collected cross-sectional images of F-actin and nuclei. A subsequent analytical step involved the scrutiny of intersecting segmented fibers between consecutive layers for geometrically accurate actin fiber reconstruction. We analyzed 102 control and 110 vibration-treated mesenchymal stem cells (MSCs) by utilizing our deep-learning-based reconstruction algorithm on confocal Z-stack images. Our data revealed an 8% decrease in nuclear height from 5.73 µm to 5.28 µm (p < 0.001) upon application of low-intensity vibration. In contrast, the two groups observed no significant difference in nuclear volume. Accompanying these changes at the nuclear level, the following changes in the F-actin cytoskeleton were observed upon LIV application. The total number of actin filaments per cell increased from 117 to 150. The average volume of individual actin fibers rose from 0.40 µm³ to 0.43 µm³. Notably, the volume of fibers at the apical nuclear surface increased from 0.35 µm³ to 0.46 µm³. While the average length of actin fibers in the apical nuclear surface remained largely unchanged due to the addition of smaller fibers in LIV groups, the LIV group had a larger number of fibers that were larger than 15µm.

3D printing assisted surface patterning process on acrylated hydrogels for contact guidance of fibroblasts

Generating stable and customizable topography on hydrogel surfaces with contact guidance potential is critical as it can direct/influence cell growth. This necessitates the development of new techniques for surface patterning of the hydrogels. We report on the design of a square grid template for surface patterning hydrogels. The template was 3-D printed and has the diameter of a well in a 24-well plate. Hyaluronic acid methacrylate (HA) hydrogel precursor solutions were cast on the 3D printed template's surface, which generated 3D square shape topographies on the HA hydrogel surface upon demolding. The 3D Laser Microscopy has shown the formation of a periodic array of 3D topographies on hydrogel surfaces. 3D Laser and Electron Microscopy Imaging have revealed that this new method has increased the surface area and exposed the underlying pore structure of the HA hydrogels. To demonstrate the method's versatility, we have successfully applied this technique to generate 3D topography on two more acrylate hydrogel formulations, gelatin Methacrylate and polyethylene glycol dimethacrylate. Human neonatal dermal fibroblast cells were used as a model cell line to evaluate the cell guidance potential of patterned HA hydrogel. Confocal fluorescence microscopy imaging has revealed that the 3D surface topographies on HA hydrogels can guide and align the actin filaments of the fibroblasts presumably due to the contact guidance mechanism. The newly developed methodology of 3D topography generation in acrylate hydrogels may influence the cell responses on hydrogel surfaces which can impact biomedical applications such as tissue engineering, wound healing, and disease modeling.

Time-of-flight resolved stimulated Raman scattering microscopy using counter-propagating ultraslow Bessel light bullets generation

We present a novel time-of-flight resolved Bessel light bullet-enabled stimulated Raman scattering (B2-SRS) microscopy for deeper tissue 3D chemical imaging with high resolution without a need for mechanical z-scanning. To accomplish the tasks, we conceive a unique method to enable optical sectioning by generating the counter-propagating pump and Stokes Bessel light bullets in the sample, in which the group velocities of the Bessel light bullets are made ultraslow (e.g., vg ≈ 0.1c) and tunable by introducing programmable angular dispersions with a spatial light modulator. We theoretically analyze the working principle of the collinear multicolor Bessel light bullet generations and velocity controls with the relative time-of-flight resolved detection for SRS 3D deep tissue imaging. We have also built the B2-SRS imaging system and present the first demonstration of B2-SRS microscopy with Bessel light bullets for 3D chemical imaging in a variety of samples (e.g., polymer bead phantoms, biological samples such as spring onion tissue and porcine brain) with high resolution. The B2-SRS technique provides a > 2-fold improvement in imaging depth in porcine brain tissue compared to conventional SRS microscopy. The method of optical sectioning in tissue using counter-propagating ultraslow Bessel light bullets developed in B2-SRS is generic and easy to perform and can be readily extended to other nonlinear optical imaging modalities to advance 3D microscopic imaging in biological and biomedical systems and beyond.

A ridge-based detection algorithm with filament overlap identification for 2D mycelium network analysis

Many terrestrial ecosystems engage mycorrhizal symbiotic associations, potentially to enhance nutrition, increase resistance to soil-borne pests and diseases, and improve resilience and soil structure. Mycorrhizal fungi create dynamic networked structures through branching and anastomosis that connect multiple plants and consent to transport resources underground from nutrient-rich patches to demanding plants. Controlled laboratory experiments are fundamental to improving our knowledge of mycelium network growth dynamics and further understanding its role in preserving ecological niches. We propose a method for highly automated analysis of the mycelium network structure and other morphological properties, such as hyphal length, hyphal density, and number of crossing and branches, in 2D microscopy images of fungal samples. Available tools for automated network analyses suffer from overestimating network connectivity since filament crossings are not considered. In particular, we propose a) a ridge-based mycelium detection algorithm and b) a geometrical-based approach to identify overlapping filaments crossing each other. The algorithmic solution is evaluated on a total of 135 real mycelium sample images over different validation steps, originating from different datasets and having different characteristics, including background, contrast, image acquisition system, fungal species, and clearness (e.g., level of transparency, homogeneity, dirtiness of the medium) of the sample. Results show that 1) the proposed detection method can be used to measure the length of mycelium in an image, replacing manual tracing and allowing for less laborious analysis ρ̂c=0.96, 2) the filament detection is on par with state-of-the-art techniques F1=0.88−0.94 with a more intuitive parameterization, and 3) the proposed algorithm correctly identifies filament crossings F1=0.89 in most common cases, yielding a reduction in the overestimation of network connectivity. The latter feature consents to applying the proposed fully automated solution to complex and irregular fungal structures, advancing mycelium detection and reconstruction performance accuracy with respect to the state-of-the-art.

Protocrystallinity of Monodispersed Ultra-Small Templated Mesoporous Silica Nanoparticles.

Monodisperse and semi-faceted ultra-small templated mesoporous silica nanoparticles (US-MSNs) of 20-25 nm were synthesized using short-time hydrolysis of tetraethoxysilane (TEOS) at room temperature, followed by a dilution for nucleation quenching. According to dynamic light scattering (DLS), a two-step pH adjustment was necessary for growth termination and colloidal stabilization. The pore size was controlled by cetyltrimethylammonium bromide (CTAB), and a tiny amount of neutral surfactant F127 was added to minimize the coalescence between US-MSNs and to favor the transition towards internal ordering. Flocculation eventually occurred, allowing us to harvest a powder by centrifugation (~60% silica yield after one month). Scanning transmission electron microscopy (STEM) and 3D high-resolution transmission electron microscopy (3D HR-TEM) images revealed that the US-MSNs are partially ordered. The 2D FT transform images provide evidence for the coexistence of four-, five-, and sixfold patterns characterizing an "on-the-edge" crystallization step between amorphous raspberry and hexagonal pore array morphologies, typical of a protocrystalline state. Calcination preserved this state and yielded a powder characterized by packing, developing a hierarchical porosity centered at 3.9 ± 0.2 (internal pores) and 68 ± 7 nm (packing voids) of high potential for support for separation and catalysis.

3DVascNet: An Automated Software for Segmentation and Quantification of Mouse Vascular Networks in 3D.

Analysis of vascular networks is an essential step to unravel the mechanisms regulating the physiological and pathological organization of blood vessels. So far, most of the analyses are performed using 2-dimensional projections of 3-dimensional (3D) networks, a strategy that has several obvious shortcomings. For instance, it does not capture the true geometry of the vasculature and generates artifacts on vessel connectivity. These limitations are accepted in the field because manual analysis of 3D vascular networks is a laborious and complex process that is often prohibitive for large volumes. To overcome these issues, we developed 3DVascNet, a deep learning-based software for automated segmentation and quantification of 3D retinal vascular networks. 3DVascNet performs segmentation based on a deep learning model, and it quantifies vascular morphometric parameters such as vessel density, branch length, vessel radius, and branching point density. We tested the performance of 3DVascNet using a large data set of 3D microscopy images of mouse retinal blood vessels. We demonstrated that 3DVascNet efficiently segments vascular networks in 3D and that vascular morphometric parameters capture phenotypes detected by using manual segmentation and quantification in 2 dimension. In addition, we showed that, despite being trained on retinal images, 3DVascNet has high generalization capability and successfully segments images originating from other data sets and organs. Overall, we present 3DVascNet, a freely available software that includes a user-friendly graphical interface for researchers with no programming experience, which will greatly facilitate the ability to study vascular networks in 3D in health and disease. Moreover, the source code of 3DVascNet is publicly available, thus it can be easily extended for the analysis of other 3D vascular networks by other users.

3D Poissonian image deblurring via patch-based tensor logarithmic Schatten-p minimization

In medical and biological image processing, multi-dimensional images are often corrupted by blur and Poisson noise. In this paper, we first propose a new tensor logarithmic Schatten-p (t-log-Sp ) low-rank measure and a tensor iteratively reweighted Schatten-p minimization algorithm for minimizing such measure. Furthermore, we adopt this low-rank measure to regularize the non-local tensors formed by similar 3D image patches and develop a patch-based non-local low-rank model. The data fidelity term of the model characterizes the Poisson noise distribution and blur operator. The optimization model is further solved by an alternating minimization technique combined with variable splitting. Experimental results tested on 3D fluorescence microscope images show that the proposed patch-based tensor logarithmic Schatten-p minimization method outperforms state-of-the-art methods in terms of image evaluation metrics and visual quality.

Automated segmentation and recognition of C. elegans whole-body cells.

Accurate segmentation and recognition of C.elegans cells are critical for various biological studies, including gene expression, cell lineages, and cell fates analysis at single-cell level. However, the highly dense distribution, similar shapes, and inhomogeneous intensity profiles of whole-body cells in 3D fluorescence microscopy images make automatic cell segmentation and recognition a challenging task. Existing methods either rely on additional fiducial markers or only handle a subset of cells. Given the difficulty or expense associated with generating fiducial features in many experimental settings, a marker-free approach capable of reliably segmenting and recognizing C.elegans whole-body cells is highly desirable. We report a new pipeline, called automated segmentation and recognition (ASR) of cells, and applied it to 3D fluorescent microscopy images of L1-stage C.elegans with 558 whole-body cells. A novel displacement vector field based deep learning model is proposed to address the problem of reliable segmentation of highly crowded cells with blurred boundary. We then realize the cell recognition by encoding and exploiting statistical priors on cell positions and structural similarities of neighboring cells. To the best of our knowledge, this is the first method successfully applied to the segmentation and recognition of C.elegans whole-body cells. The ASR-segmentation module achieves an F1-score of 0.8956 on a dataset of 116 C.elegans image stacks with 64 728 cells (accuracy 0.9880, AJI 0.7813). Based on the segmentation results, the ASR recognition module achieved an average accuracy of 0.8879. We also show ASR's applicability to other cell types, e.g. platynereis and rat kidney cells. The code is available at https://github.com/reaneyli/ASR.

Adaptive focus stacking for large depth-of-field microscopic structured-light 3D imaging

This paper presents an adaptive focus stacking method for large depth-of-field (DOF) 3D microscopic structured-light imaging systems. Conventional focus stacking methods typically capture images under a series of pre-defined focus settings without considering the attributes of the measured object. Therefore, it is inefficient since some of the focus settings might be redundant. To address this problem, we first employ the focal sweep technique to reconstruct an initial rough 3D shape of the measured objects. Then, we leverage the initial 3D data to determine effective focus settings that focus the camera on the valid areas of the measured objects. Finally, we reconstruct a high-quality 3D point cloud using fringe images obtained from these effective focus settings by focus stacking. Experimental results demonstrate the success of the proposed method.

Deep learning-based vessel extraction in 3D confocal microscope images of cleared human glioma tissues.

Comprehensive visualization and accurate extraction of tumor vasculature are essential to study the nature of glioma. Nowadays, tissue clearing technology enables 3D visualization of human glioma vasculature at micron resolution, but current vessel extraction schemes cannot well cope with the extraction of complex tumor vessels with high disruption and irregularity under realistic conditions. Here, we developed a framework, FineVess, based on deep learning to automatically extract glioma vessels in confocal microscope images of cleared human tumor tissues. In the framework, a customized deep learning network, named 3D ResCBAM nnU-Net, was designed to segment the vessels, and a novel pipeline based on preprocessing and post-processing was developed to refine the segmentation results automatically. On the basis of its application to a practical dataset, we showed that the FineVess enabled extraction of variable and incomplete vessels with high accuracy in challenging 3D images, better than other traditional and state-of-the-art schemes. For the extracted vessels, we calculated vascular morphological features including fractal dimension and vascular wall integrity of different tumor grades, and verified the vascular heterogeneity through quantitative analysis.

Reorganization of mitochondria-organelle interactions during postnatal development in skeletal muscle.

Skeletal muscle cellular development requires the integrated assembly of mitochondria and other organelles adjacent to the sarcomere in support of muscle contractile performance. However, it remains unclear how interactions among organelles and with the sarcomere relates to the development of muscle cell function. Here, we combine 3D volume electron microscopy, proteomic analyses, and live cell functional imaging to investigate the postnatal reorganization of mitochondria-organelle interactions in skeletal muscle. We show that while mitochondrial networks are disorganized and loosely associated with the contractile apparatus at birth, contact sites among mitochondria, lipid droplets and the sarcoplasmic reticulum are highly abundant in neonatal muscles. The maturation process is characterized by a transition to highly organized mitochondrial networks wrapped tightly around the muscle sarcomere but also to less frequent interactions with both lipid droplets and the sarcoplasmic reticulum. Concomitantly, expression of proteins involved in mitochondria-organelle membrane contact sites decreases during postnatal development in tandem with a decrease in abundance of proteins associated with sarcomere assembly despite an overall increase in contractile protein abundance. Functionally, parallel measures of mitochondrial membrane potential, NADH redox status, and NADH flux within intact cells revealed that mitochondria in adult skeletal muscle fibres maintain a more activated electron transport chain compared with neonatal muscle mitochondria. These data demonstrate a developmental redesign reflecting a shift from muscle cell assembly and frequent inter-organelle communication toward a muscle fibre with mitochondrial structure, interactions, composition and function specialized to support contractile function. KEY POINTS: Mitochondrial network organization is remodelled during skeletal muscle postnatal development. The mitochondrial outer membrane is in frequent contact with other organelles at birth and transitions to more close associations with the contractile apparatus in mature muscles. Mitochondrial energy metabolism becomes more activated during postnatal development. Understanding the developmental redesign process within skeletal muscle cells may help pinpoint specific areas of deficit in muscles with developmental disorders.

Data-Driven and Cell-Specific Determination of Nuclei-Associated Actin Structure.

Quantitative and volumetric assessment of filamentous actin fibers (F-actin) remains challenging due to their interconnected nature, leading researchers to utilize threshold based or qualitative measurement methods with poor reproducibility. Here we introduce a novel machine learning based methodology for accurate quantification and reconstruction of nuclei-associated F-actin. Utilizing a Convolutional Neural Network (CNN), we segment actin filaments and nuclei from 3D confocal microscopy images and then reconstruct each fiber by connecting intersecting contours on cross-sectional slices. This allowed measurement of the total number of actin filaments and individual actin filament length and volume in a reproducible fashion. Focusing on the role of F-actin in supporting nucleocytoskeletal connectivity, we quantified apical F-actin, basal F-actin, and nuclear architecture in mesenchymal stem cells (MSCs) following the disruption of the Linker of Nucleoskeleton and Cytoskeleton (LINC) Complexes. Disabling LINC in mesenchymal stem cells (MSCs) generated F-actin disorganization at the nuclear envelope characterized by shorter length and volume of actin fibers contributing a less elongated nuclear shape. Our findings not only present a new tool for mechanobiology but introduce a novel pipeline for developing realistic computational models based on quantitative measures of F-actin.

Quantifying nanoscopic alterations associated with mitochondrial dysfunction using three-dimensional single-molecule localization microscopy.

Mitochondrial morphology provides unique insights into their integrity and function. Among fluorescence microscopy techniques, 3D super-resolution microscopy uniquely enables the analysis of mitochondrial morphological features individually. However, there is a lack of tools to extract morphological parameters from super-resolution images of mitochondria. We report a quantitative method to extract mitochondrial morphological metrics, including volume, aspect ratio, and local protein density, from 3D single-molecule localization microscopy images, with single-mitochondrion sensitivity. We validated our approach using simulated ground-truth SMLM images of mitochondria. We further tested our morphological analysis on mitochondria that have been altered functionally and morphologically in controlled manners. This work sets the stage to quantitatively analyze mitochondrial morphological alterations associated with disease progression on an individual basis.

Data Science for Health Image Alignment: A User-Friendly Open-Source ImageJ/Fiji Plugin for Aligning Multimodality/Immunohistochemistry/Immunofluorescence 2D Microscopy Images.

Most of the time, the deep analysis of a biological sample requires the acquisition of images at different time points, using different modalities and/or different stainings. This information gives morphological, functional, and physiological insights, but the acquired images must be aligned to be able to proceed with the co-localisation analysis. Practically speaking, according to Aristotle's principle, "The whole is greater than the sum of its parts", multi-modal image registration is a challenging task that involves fusing complementary signals. In the past few years, several methods for image registration have been described in the literature, but unfortunately, there is not one method that works for all applications. In addition, there is currently no user-friendly solution for aligning images that does not require any computer skills. In this work, DS4H Image Alignment (DS4H-IA), an open-source ImageJ/Fiji plugin for aligning multimodality, immunohistochemistry (IHC), and/or immunofluorescence (IF) 2D microscopy images, designed with the goal of being extremely easy to use, is described. All of the available solutions for aligning 2D microscopy images have also been revised. The DS4H-IA source code; standalone applications for MAC, Linux, and Windows; video tutorials; manual documentation; and sample datasets are publicly available.

Pairing-specific microstructure in depletion gels of bidisperse colloids.

We report the ensemble-averaged and pairing-specific network microstructure formed by short-range depletion attractions in hard sphere-like colloidal systems. Gelation is induced by adding polystyrene molecules at a fixed concentration to colloids with different colloid bidispersity ratios (α = 1, 0.72, and 0.60) across a range of volume fractions (0.10 ≤ ϕ ≤ 0.40). 3D confocal microscopy imaging combined with a scale-invariant feature transform algorithm show that monodisperse colloids pack more efficiently, whereas increasing the size disparity leads to looser, more disordered, and sub-isostatic packings. Categorizing the structures formed by small and large particles reveal that certain cluster configurations may be favored due to the complex interplay between the differences in particle surface areas and attractive potentials. These pairwise bonds assemble to affect the density of tetrahedral and poly-tetrahedral clusters in bidisperse systems. With the exception of non-percolating samples at ϕ = 0.10, increasing the gel volume fraction leads to an increase in the number of nearest neighbors. However, the internal density within each cluster decreases, possibly due to kinetic arrest from the deeper potential wells of tetrahedral clusters at low volume fractions in which vertices are primarily made out of larger particles.

DNeuroMAT: A Deep-Learning-Based Neuron Morphology Analysis Toolbox.

Digital reconstruction of neuronal structures from 3D neuron microscopy images is critical for the quantitative investigation of brain circuits and functions. Currently, neuron reconstructions are mainly obtained by manual or semiautomatic methods. However, these ways are labor-intensive, especially when handling the huge volume of whole brain microscopy imaging data. Here, we present a deep-learning-based neuron morphology analysis toolbox (DNeuroMAT) for automated analysis of neuron microscopy images, which consists of three modules: neuron segmentation, neuron reconstruction, and neuron critical points detection.

Topology-based segmentation of 3D confocal images of emerging hematopoietic stem cells in the zebrafish embryo

Abstract We develop a novel method for image segmentation of 3D confocal microscopy images of emerging hematopoietic stem cells. The method is based on the theory of persistent homology and uses an optimal threshold to select the most persistent cycles in the persistence diagram. This enables the segmentation of the image’s most contrasted and representative shapes. Coupling this segmentation method with a meshing algorithm, we define a pipeline for 3D reconstruction of confocal volumes. Compared to related methods, this approach improves shape segmentation, is more ergonomic to automatize, and has fewer parameters. We apply it to the segmentation of membranes, at subcellular resolution, of cells involved in the endothelial-to-hematopoietic transition (EHT) in the zebrafish embryos.

Correlative atomic force microscopy and scanning electron microscopy of bacteria-diamond-metal nanocomposites

Research investigating the interface between biological organisms and nanomaterials nowadays requires multi-faceted microscopic methods to elucidate the interaction mechanisms and effects. Here we describe a novel approach and methodology correlating data from an atomic force microscope inside a scanning electron microscope (AFM-in-SEM). This approach is demonstrated on bacteria-diamond-metal nanocomposite samples relevant in current life science research. We describe a procedure for preparing such multi-component test samples containing E. coli bacteria and chitosan-coated hydrogenated nanodiamonds decorated with silver nanoparticles on a carbon-coated gold grid. Microscopic topography information (AFM) is combined with chemical, material, and morphological information (SEM using SE and BSE at varied acceleration voltages) from the same region of interest and processed to create 3D correlative probe-electron microscopy (CPEM) images. We also establish a novel 3D RGB color image algorithm for merging multiple SE/BSE data from SEM with the AFM surface topography data which provides additional information about microscopic interaction of the diamond-metal nanocomposite with bacteria, not achievable by individual analyses. The methodology of CPEM data interpretation is independently corroborated by further in-situ (EDS) and ex-situ (micro-Raman) chemical characterization as well as by force volume AFM analysis. We also discuss the broader applicability and benefits of the methodology for life science research.

Electrically tunable lens assisted absolute phase unwrapping for large depth-of-field 3D microscopic structured-light imaging

This paper presents an absolute phase unwrapping method for large depth-of-field (DOF) microscopic structured-light 3D imaging. We calculate the wrapped phase maps and the fringe contrast maps from fringe images captured under various focus settings realized by an electrically tunable lens. From the fringe contrast maps, we extract each in-focus pixel and determine its approximate depth value from the focus settings. The approximate depth map is then used to create an artificial phase map. The geometric-constraint-based phase unwrapping method is adopted to unwrap the phase of all in-focus pixels using the artificial phase map. The unwrapped in-focus phase map is then used to reconstruct 3D coordinates for the entire DOF with the multi-focus pin-hole model. Experimental results demonstrate that our proposed method only requires three phase-shifted fringe patterns to achieve large DOF 3D imaging.