3D Microscopy Images Research Articles

Segmentation of Nuclei From 3D Microscopy Images of Tissue via Graphcut Optimization

Nuclear segmentation in 3D microscopic tissue images remains a challenge due to tight packing of the cells, low contrast and poor depth resolution. To address this problem, we developed a robust and accurate nuclear segmentation algorithm suited for tissue samples of higher organisms such as mice and humans. The study was inspired by previous works in graphcut-based segmentation in areas other than optical microscopy. We propose a novel seed detection method for nuclei in 3D tissue images that uses a robust model-based 2D slice by slice segmentation followed by a non-maximal suppression like algorithm for selection of the most prospective set of seeds. After seed detection, for each target nucleus the method transformed the microscopic volume to a geometric volume in spherical space with respect to the respective seed point and found the globally optimal surface in that geometric volume which separated the target cell nucleus from the rest of the volume using a graphcut-based algorithm. Comparing the automatic segmentation maps obtained by the proposed method to those obtained from a number of state-of-the art methods demonstrate superior robustness and accuracy of the proposed method in terms of three evaluation metrics. Along with the automatic method, we also present an interactive version of the algorithm.

Unravelling the potential of nitric acid as a surface modifier for improving the hemocompatibility of metallocene polyethylene for blood contacting devices.

Design of blood compatible surfaces is obligatory to minimize platelet surface interactions and improve the thromboresistance of foreign surfaces when they are utilized as biomaterials particularly for blood contacting devices. Pure metallocene polyethylene (mPE) and nitric acid (HNO3) treated mPE antithrombogenicity and hydrophilicity were investigated. The contact angle of the mPE treated with HNO3 decreased. Surface of mPE and HNO3 treated mPE investigated with FTIR revealed no major changes in its functional groups. 3D Hirox digital microscopy, SEM and AFM images show increased porosity and surface roughness. Blood coagulation assays prothrombin time (PT) and activated partial thromboplastin time (APTT) were delayed significantly (P < 0.05) for HNO3 treated mPE. Hemolysis assay and platelet adhesion of the treated surface resulted in the lysis of red blood cells and platelet adherence, respectively indicating improved hemocompatibility of HNO3 treated mPE. To determine that HNO3 does not deteriorate elastic modulus of mPE, the elastic modulus of mPE and HNO3 treated mPE was compared and the result shows no significant difference. Hence, the overall observation suggests that the novel HNO3 treated mPE may hold great promises to be exploited for blood contacting devices like grafts, catheters, and etc.

Abstract B103: Transparent tumor tomography (T3): Spatial 3D mapping of immune responses in a whole tumor after immunotherapy

Abstract This research seeks to develop an imaging technique for 3D mapping of immune contexts within a tumor. Capability to determine location, density, and functional orientation of the different immune cell populations within a whole tumor would be a powerful tool to examine crosstalk between a tumor microenvironment and immune system but also to advance development of effective cancer immunotherapy strategies. Although immunofluorescence staining has long been used in cancer immunology, the conventional immunofluorescence method is restricted to the localization of targeted antigens in limited two-dimensional (2D) tumor sections or small tumor volumes. Thus, 2D immunofluorescence cannot provide integrated spatial information about the immune cell distributions or other features of tumor tissue, leaving room to misinterpret the extent or significance of responses to therapy or downstream effects. To overcome these challenges, we developed a three-dimensional (3D) immunofluorescence tomography approach to map whole tumors at single cell resolution. Thus, we optimized optical clearing of tumor macroslices, increased penetration of antibodies to permit immunofluorescence staining, applied multicolor 3D microscopy imaging, enabled tomographic reconstruction of the images, and implemented automated image analysis. This approach allows us to spatially and quantitatively determine the distribution of tumor-infiltrating T cells (CD4+, CD8+, and regulatory (CD4+CD25+Foxp3+) T cells) in whole mouse mammary (TUBO) tumors. To demonstrate utility in evaluating immune responses, we performed intratumoral injection of the STING agonist DMXAA and evaluated anti-tumor immune responses in the whole tumor, including detecting damage to the microvasculature using the CD31 endothelial marker and increase in tumor-infiltrating CD8+ T cells. Our results establish a new method, Transparent Tumor Tomography (T3), that provides integrated spatial information regarding immune contexts in whole tumors as a new tool for cancer immunotherapy research. Citation Format: Steve Seung-Young Lee, Vytas Bindokas, Stephen J. Kron. Transparent tumor tomography (T3): Spatial 3D mapping of immune responses in a whole tumor after immunotherapy. [abstract]. In: Proceedings of the CRI-CIMT-EATI-AACR Inaugural International Cancer Immunotherapy Conference: Translating Science into Survival; September 16-19, 2015; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2016;4(1 Suppl):Abstract nr B103.

A Framework for Segmentation of Inhomogeneous Live Cell Images using Fractional Derivatives and Level Set Method

Cell segmentation has gained significant importance in modern biological image processing applications. The commonly used image segmentation algorithms are region based and depend on the homogeneity of the intensities of the pixels in the region of interest. But due to the highly inhomogeneous behavior of cell nuclei and background, feature overlapping between the two regions lead to misclassification and poor segmentation results. This paper proposes a method to segment the cell images taking into consideration the intensity inhomogeneity issue. A fractional differential term has been introduced in the clustering criteria for bias correction for improving the homogeneity of the cell images. A method to optimize the fractional order for images has also been proposed. Further an improved narrow band level set method using Chan Vese model has been proposed to improve the computational speed of the algorithm. The proposed method is evaluated on datasets of 2D microscopy images and images with improved homogeneity have been obtained. The results also show improved segmentation results and the time efficient bahaviour of the proposed method.

TReMAP: Automatic 3D Neuron Reconstruction Based on Tracing, Reverse Mapping and Assembling of 2D Projections.

Efficient and accurate digital reconstruction of neurons from large-scale 3D microscopic images remains a challenge in neuroscience. We propose a new automatic 3D neuron reconstruction algorithm, TReMAP, which utilizes 3D Virtual Finger (a reverse-mapping technique) to detect 3D neuron structures based on tracing results on 2D projection planes. Our fully automatic tracing strategy achieves close performance with the state-of-the-art neuron tracing algorithms, with the crucial advantage of efficient computation (much less memory consumption and parallel computation) for large-scale images.

Morphological analysis and feature extraction of neurons from mouse cortices multiscale 3D microscopic images.

In this paper, we propose a framework to analyze the morphology of mouse neurons in the layer V of the cortex from 3D microscopic images. We are given 8 sets of images, each of which is composed of a 10x image showing the whole neurons, and a few (2 to 5) 40x images focusing on the somas. The framework consists in segmenting the neurons on both types of images to compute a set of specific morphological features, and in matching the neurons in the 40x images to their counterparts in the 10x images to combine the features we obtained, in a fully automatic fashion.

Performance and sensitivity evaluation of 3D spot detection methods in confocal microscopy.

Reliable 3D detection of diffraction-limited spots in fluorescence microscopy images is an important task in subcellular observation. Generally, fluorescence microscopy images are heavily degraded by noise and non-specifically stained background, making reliable detection a challenging task. In this work, we have studied the performance and parameter sensitivity of eight recent methods for 3D spot detection. The study is based on both 3D synthetic image data and 3D real confocal microscopy images. The synthetic images were generated using a simulator modeling the complete imaging setup, including the optical path as well as the image acquisition process. We studied the detection performance and parameter sensitivity under different noise levels and under the influence of uneven background signal. To evaluate the parameter sensitivity, we propose a novel measure based on the gradient magnitude of the F1 score. We measured the success rate of the individual methods for different types of the image data and found that the type of image degradation is an important factor. Using the F1 score and the newly proposed sensitivity measure, we found that the parameter sensitivity is not necessarily proportional to the success rate of a method. This also provided an explanation why the best performing method for synthetic data was outperformed by other methods when applied to the real microscopy images. On the basis of the results obtained, we conclude with the recommendation of the HDome method for data with relatively low variations in quality, or the Sorokin method for image sets in which the quality varies more. We also provide alternative recommendations for high-quality images, and for situations in which detailed parameter tuning might be deemed expensive.

Static real-time capture of 3D microscopy images

Static real-time capture of 3D microscopy images

AUTOMATED CELL SEGMENTATION WITH 3D FLUORESCENCE MICROSCOPY IMAGES.

A large number of cell-oriented cancer investigations require an effective and reliable cell segmentation method on three dimensional (3D) fluorescence microscopic images for quantitative analysis of cell biological properties. In this paper, we present a fully automated cell segmentation method that can detect cells from 3D fluorescence microscopic images. Enlightened by fluorescence imaging techniques, we regulated the image gradient field by gradient vector flow (GVF) with interpolated and smoothed data volume, and grouped voxels based on gradient modes identified by tracking GVF field. Adaptive thresholding was then applied to voxels associated with the same gradient mode where voxel intensities were enhanced by a multiscale cell filter. We applied the method to a large volume of 3D fluorescence imaging data of human brain tumor cells with (1) small cell false detection and missing rates for individual cells; and (2) trivial over and under segmentation incidences for clustered cells. Additionally, the concordance of cell morphometry structure between automated and manual segmentation was encouraging. These results suggest a promising 3D cell segmentation method applicable to cancer studies.

Reflected Beam Light-Sheet Microscopy for Whole-Cell 3D Super-Resolution Imaging

Single molecule super-resolution (SM-SR) imaging is performed by localizing individual fluorophores within a densely labeled sample. SM-SR in 3D throughout a whole cell is hampered by wide-field activation because imaging light cannot be targeted to the in-focus image plane as done with TIRF. This results in high background fluorescence that severely degrades detection and localization precision. We have developed a technique, which we termed Reflected Beam Light-Sheet Microscopy (RB-LSM), that uses a common, single objective, inverted epi-fluorescence microscope along with a reflective surface to generate illumination only in the image focal plane. We use a reflective, planar surface which is placed at an angle of 45° on a cover slip to reflect the beam. This surface forms the side wall of a microfluidic channel incorporated into a microfluidic device. A light-sheet is generated through the objective and reflected by the surface such that it illuminates only the in-focal plane of the cell. The light-sheet is scanned through the cell for whole-cell imaging and an astigmatic lens in the emission light path enables the 3D localization of individual emitters, creating a whole-cell 3D super-resolution image. RB-LSM provides unprecedented localization accuracy due to ∼20 fold background reduction. An additional benefit comes from reduced photobleaching of un-imaged, out of focus fluorophores. The microfluidics device provides a closed environment that allows for fast and automated buffer exchange, which is utilized to maintain a reduced oxygen environment needed for many SM-SR probes. The RB-LSM is a system that is easily implemented on a regular wide-field fluorescence microscope. The development of this microscope and its imaging and analysis routines will therefore be of great value to investigators seeking a relatively straightforward, inexpensive method for whole-cell 3D super-resolution microscopy and high-speed live cell imaging.

Alginate microencapsulated hepatocytes optimised for transplantation in acute liver failure.

Background and AimIntraperitoneal transplantation of alginate-microencapsulated human hepatocytes is an attractive option for the management of acute liver failure (ALF) providing short-term support to allow native liver regeneration. The main aim of this study was to establish an optimised protocol for production of alginate-encapsulated human hepatocytes and evaluate their suitability for clinical use.MethodsHuman hepatocyte microbeads (HMBs) were prepared using sterile GMP grade materials. We determined physical stability, cell viability, and hepatocyte metabolic function of HMBs using different polymerisation times and cell densities. The immune activation of peripheral blood mononuclear cells (PBMCs) after co-culture with HMBs was studied. Rats with ALF induced by galactosamine were transplanted intraperitoneally with rat hepatocyte microbeads (RMBs) produced using a similar optimised protocol. Survival rate and biochemical profiles were determined. Retrieved microbeads were evaluated for morphology and functionality.ResultsThe optimised HMBs were of uniform size (583.5±3.3 µm) and mechanically stable using 15 min polymerisation time compared to 10 min and 20 min (p<0.001). 3D confocal microscopy images demonstrated that hepatocytes with similar cell viability were evenly distributed within HMBs. Cell density of 3.5×106 cells/ml provided the highest viability. HMBs incubated in human ascitic fluid showed better cell viability and function than controls. There was no significant activation of PBMCs co-cultured with empty or hepatocyte microbeads, compared to PBMCs alone. Intraperitoneal transplantation of RMBs was safe and significantly improved the severity of liver damage compared to control groups (empty microbeads and medium alone; p<0.01). Retrieved RMBs were intact and free of immune cell adherence and contained viable hepatocytes with preserved function.ConclusionAn optimised protocol to produce GMP grade alginate-encapsulated human hepatocytes has been established. Transplantation of microbeads provided effective metabolic function in ALF. These high quality HMBs should be suitable for use in clinical transplantation.

From Curves to Trees: A Tree-like Shapes Distance Using the Elastic Shape Analysis Framework.

Trees are a special type of graph that can be found in various disciplines. In the field of biomedical imaging, trees have been widely studied as they can be used to describe structures such as neurons, blood vessels and lung airways. It has been shown that the morphological characteristics of these structures can provide information on their function aiding the characterization of pathological states. Therefore, it is important to develop methods that analyze their shape and quantify differences between their structures. In this paper, we present a method for the comparison of tree-like shapes that takes into account both topological and geometrical information. This method, which is based on the Elastic Shape Analysis Framework, also computes the mean shape of a population of trees. As a first application, we have considered the comparison of axon morphology. The performance of our method has been evaluated on two sets of images. For the first set of images, we considered four different populations of neurons from different animals and brain sections from the NeuroMorpho.org open database. The second set was composed of a database of 3D confocal microscopy images of three populations of axonal trees (normal and two types of mutations) of the same type of neurons. We have calculated the inter and intra class distances between the populations and embedded the distance in a classification scheme. We have compared the performance of our method against three other state of the art algorithms, and results showed that the proposed method better distinguishes between the populations. Furthermore, we present the mean shape of each population. These shapes present a more complete picture of the morphological characteristics of each population, compared to the average value of certain predefined features.

A study of the effect of surface pre-treatment on the adhesion of coatings

This paper deals with the study of effects of mechanical surface preparation on the adhesion of coating with high content of zinc dust. Five kinds of mechanically blasted surfaces were studied. The following were used as abrasives: steel shot, steel grit, brown corundum oxide and zirblast. The last surface type was modified by MBX Blaster technology (mechanical bristle blasting). The surfaces topography was quantified by a roughness profilometer. The shape and size of the incurred inequalities on the modified surfaces were studied using 3D microscope images of the surface. The purity of the surfaces after pre-treatment was evaluated by impurity glued on the tape and measuring the reflection of light from the surface. Fractal analysis was used to evaluate the diversity of inequalities on the prepared surfaces. Cross-sections were also taken of the prepared surfaces. The prepared surfaces were coated with zinc-filled coating. The adhesion of the coating to the substrate was evaluated by a pull-off test after curing the coating (as sprayed), as well as after exposure to severe corrosive environments. The best adhesion of the coating was found for the coating applied to the substrate which had been pre-treated with brown corundum and steel grit.

Current automated 3D cell detection methods are not a suitable replacement for manual stereologic cell counting

Stereologic cell counting has had a major impact on the field of neuroscience. A major bottleneck in stereologic cell counting is that the user must manually decide whether or not each cell is counted according to three-dimensional (3D) stereologic counting rules by visual inspection within hundreds of microscopic fields-of-view per investigated brain or brain region. Reliance on visual inspection forces stereologic cell counting to be very labor-intensive and time-consuming, and is the main reason why biased, non-stereologic two-dimensional (2D) “cell counting” approaches have remained in widespread use. We present an evaluation of the performance of modern automated cell detection and segmentation algorithms as a potential alternative to the manual approach in stereologic cell counting. The image data used in this study were 3D microscopic images of thick brain tissue sections prepared with a variety of commonly used nuclear and cytoplasmic stains. The evaluation compared the numbers and locations of cells identified unambiguously and counted exhaustively by an expert observer with those found by three automated 3D cell detection algorithms: nuclei segmentation from the FARSIGHT toolkit, nuclei segmentation by 3D multiple level set methods, and the 3D object counter plug-in for ImageJ. Of these methods, FARSIGHT performed best, with true-positive detection rates between 38 and 99% and false-positive rates from 3.6 to 82%. The results demonstrate that the current automated methods suffer from lower detection rates and higher false-positive rates than are acceptable for obtaining valid estimates of cell numbers. Thus, at present, stereologic cell counting with manual decision for object inclusion according to unbiased stereologic counting rules remains the only adequate method for unbiased cell quantification in histologic tissue sections.

Effect of Substrate Bias Voltage on the Physical Properties of Zirconium Nitride (ZrN) Films Deposited by Mid Frequency Reactive Magnetron Sputtering

Present work involves the preparation of Zirconium Nitride thin films on stainless steel (SS) (304L grade) substrate by reactive cylindrical magnetron sputtering method. The X-ray diffraction (XRD) profile of the ZrN thin films prepared with different bias voltage conforms face centered cubic structure with preferred orientation along the (111) plane at lower bias voltage (100 V) and at higher bias voltage (300 V) the preferred orientation shifted to (220) plane. The influences of bias voltage on the thickness and microhardness ZrN thin films have been studied. ZrN thin film sputtered with 300 V bias voltage shows the maximum reflectance of 90% at a wavelength of 1000 nm. The coated substrates have been found to exhibit improved corrosion resistance compared to the SS plate. The root mean square surface roughness and surface morphology were investigated from 3D atomic force microscope (AFM) images and scanning electron microscope (SEM), which indicate smooth and uniform surface pattern without any pin holes.

Generalized Higher Degree Total Variation (HDTV) Regularization

We introduce a family of novel image regularization penalties called generalized higher degree total variation (HDTV). These penalties further extend our previously introduced HDTV penalties, which generalize the popular total variation (TV) penalty to incorporate higher degree image derivatives. We show that many of the proposed second degree extensions of TV are special cases or are closely approximated by a generalized HDTV penalty. Additionally, we propose a novel fast alternating minimization algorithm for solving image recovery problems with HDTV and generalized HDTV regularization. The new algorithm enjoys a tenfold speed up compared with the iteratively reweighted majorize minimize algorithm proposed in a previous paper. Numerical experiments on 3D magnetic resonance images and 3D microscopy images show that HDTV and generalized HDTV improve the image quality significantly compared with TV.

Fast Segmentation of Stained Nuclei in Terabyte-Scale, Time Resolved 3D Microscopy Image Stacks

Automated analysis of multi-dimensional microscopy images has become an integral part of modern research in life science. Most available algorithms that provide sufficient segmentation quality, however, are infeasible for a large amount of data due to their high complexity. In this contribution we present a fast parallelized segmentation method that is especially suited for the extraction of stained nuclei from microscopy images, e.g., of developing zebrafish embryos. The idea is to transform the input image based on gradient and normal directions in the proximity of detected seed points such that it can be handled by straightforward global thresholding like Otsu’s method. We evaluate the quality of the obtained segmentation results on a set of real and simulated benchmark images in 2D and 3D and show the algorithm’s superior performance compared to other state-of-the-art algorithms. We achieve an up to ten-fold decrease in processing times, allowing us to process large data sets while still providing reasonable segmentation results.

3D Microscopic Imaging and Evaluation of Tubular Tissue Architecture

3D microscopy and image analysis provide reliable measurements of length, branching, density, tortuosity and orientation of tubular structures in biological samples. We present a survey of methods for analysis of large samples by measurement of local differences in geometrical characteristics. The methods are demonstrated on the structure of the capillary bed in a rat brain.

Scaled Heavy-Ball Acceleration of the Richardson-Lucy Algorithm for 3D Microscopy Image Restoration.

The Richardson-Lucy algorithm is one of the most important in image deconvolution. However, a drawback is its slow convergence. A significant acceleration was obtained using the technique proposed by Biggs and Andrews (BA), which is implemented in the deconvlucy function of the image processing MATLAB toolbox. The BA method was developed heuristically with no proof of convergence. In this paper, we introduce the heavy-ball (H-B) method for Poisson data optimization and extend it to a scaled H-B method, which includes the BA method as a special case. The method has a proof of the convergence rate of O(K(-2)), where k is the number of iterations. We demonstrate the superior convergence performance, by a speedup factor of five, of the scaled H-B method on both synthetic and real 3D images.

基于红蓝双光阑的单物镜立体显微成像系统

To obtain a 3D model of microscopic dynamic samples,a 3D microscopic imaging method based on a single objective and red-blue dual-apertures was proposed and a corresponding 3D microscopic specimen system for capturing images was established.In the system,the dual apertures covered with a red and a blue filters were respectively placed in the exit pupil plane of the objective. When it was illuminated by a white light,the color CCD in the imaging plane of objective could receive the perspective views of specimen expressed by red and blue images respectively at one exposure.The stereo image with an optical parallax for the specimen could be obtained by digital image processing, and the 3Dmodel of the specimen could be further reconstructed by aphase matching algorithm.Experimental results indicate that the system has reconstructed the 3Dmodel with a Central Depth Plane(CDP)up to 241.50μm.As compared with double-objective 3D microscopic imaging systems,the proposed system is more compact and simpler and can obtain a 3Dmodel of microscopic dynamic sample at one exposure,so that the system has important application prospects on microscopes and endoscopes.