Automated Cell Detection Research Articles

Automatic quantification of the subcellular localization of chimeric GFP protein supported by a two-level Naive Bayes classifier

Herein we report a method supported by a two-level Naive Bayes classifier to help and improve the automatic detection and counting of cells overexpressing GFP-chimeric proteins. This toll is frequently used as a reporter for the localization and the distribution pattern of a protein in a cell. This approximation requires, besides confocal microscopy, the participation of a qualified and blind counting supervisor to avoid subjective appreciations of the imaging interpretation of the data. Indeed, this counting required specific staff training, and the interpretation of the data is inevitably subjective. In order to avoid this, we have designed an automatic detection cell counting software. We have used as a model SH-SY5Y cells overexpressing GFP-Bax protein, after 6-hydroxydopamine addition. Our proposed method learns the counting criteria after a short training stage, and uses the resulting classifier to process new images and obtaining both the number of transfected cells and the proportion of these cells that present a translocated protein. The software achieves an accuracy over 97% when detecting transfected cells, and over 93% when detecting cells with GFP-Bax translocated. Besides the hours of qualified work that can be saved, the models learnt can be stored and reused (without training) so as to homogenize criteria among different researchers.

Intelligent CAD System for Automatic Detection of Mitotic Cells from Breast Cancer Histology Slide Images Based on Teaching-Learning-Based Optimization

This paper introduces a computer-assisted diagnosis (CAD) system for automatic mitosis detection from breast cancer histopathology slide images. In this system, a new approach for reducing the number of false positives is proposed based on Teaching-Learning-Based optimization (TLBO). The proposed CAD system is implemented on the histopathology slide images acquired by Aperio XT scanner (scanner A). In TLBO algorithm, the number of false positives (falsely detected nonmitosis candidates as mitosis ones) is defined as a cost function and, by minimizing it, many of nonmitosis candidates will be removed. Then some color and texture (textural) features such as those derived from cooccurrence and run-length matrices are extracted from the remaining candidates and finally mitotic cells are classified using a specific support vector machine (SVM) classifier. The simulation results have proven the claims about the high performance and efficiency of the proposed CAD system.

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.

Automated Cell Detection and Morphometry on Growth Plate Images of Mouse Bone.

Microscopy imaging of mouse growth plates is extensively used in biology to understand the effect of specific molecules on various stages of normal bone development and on bone disease. Until now, such image analysis has been conducted by manual detection. In fact, when existing automated detection techniques were applied, morphological variations across the growth plate and heterogeneity of image background color, including the faint presence of cells (chondrocytes) located deeper in tissue away from the image's plane of focus, and lack of cell-specific features, interfered with identification of cell. We propose the first method of automated detection and morphometry applicable to images of cells in the growth plate of long bone. Through ad hoc sequential application of the Retinex method, anisotropic diffusion and thresholding, our new cell detection algorithm (CDA) addresses these challenges on bright-field microscopy images of mouse growth plates. Five parameters, chosen by the user in respect of image characteristics, regulate our CDA. Our results demonstrate effectiveness of the proposed numerical method relative to manual methods. Our CDA confirms previously established results regarding chondrocytes' number, area, orientation, height and shape of normal growth plates. Our CDA also confirms differences previously found between the genetic mutated mouse Smad1/5CKO and its control mouse on fluorescence images. The CDA aims to aid biomedical research by increasing efficiency and consistency of data collection regarding arrangement and characteristics of chondrocytes. Our results suggest that automated extraction of data from microscopy imaging of growth plates can assist in unlocking information on normal and pathological development, key to the underlying biological mechanisms of bone growth.

Automated Detection of Binucleated Cell and Micronuclei using CellProfiler 2.0 Software

Micronucleus assay in human peripheral lymphocytes usually used to assess chromosomal damage. Manual scoring of micronuclei can be time consuming and large numbers of binucleated cells have to be analyzed to obtain statistically relevant data. Automation of the micronuclei analysis using image processing analysis software can provide a faster and more reliable analysis of micronucleus assay. Here the used of CellProfiler an open access cell image analysis software for automatic detection of binucleated cells and micronuclei were reported. We aimed to know whether there was a significant difference in the number of binucleated cells and micronuclei that obtained by manual and CellProfiler counting. Wilcoxon Rank test was used for statistical analysis to test H 0 hypothesis that there was no significant difference in the number of binucleated cells and micronuclei that obtained by manual and CellProfiler counting. We analyzed 135 images for both manual and CellProfiler counting. Our results showed that there was no significant difference between manual and CellProfiler counting for binucleated cells (P = 0.851) and for micronuclei (P = 0.917). In conclusion, the binucleated cells and micronuclei counting using CellProfiler were comparable but not better than manual counting.

Automatic detection and analysis of cell motility in phase‐contrast time‐lapse images using a combination of maximally stable extremal regions and Kalman filter approaches

Phase-contrast illumination is simple and most commonly used microscopic method to observe nonstained living cells. Automatic cell segmentation and motion analysis provide tools to analyze single cell motility in large cell populations. However, the challenge is to find a sophisticated method that is sufficiently accurate to generate reliable results, robust to function under the wide range of illumination conditions encountered in phase-contrast microscopy, and also computationally light for efficient analysis of large number of cells and image frames. To develop better automatic tools for analysis of low magnification phase-contrast images in time-lapse cell migration movies, we investigated the performance of cell segmentation method that is based on the intrinsic properties of maximally stable extremal regions (MSER). MSER was found to be reliable and effective in a wide range of experimental conditions. When compared to the commonly used segmentation approaches, MSER required negligible preoptimization steps thus dramatically reducing the computation time. To analyze cell migration characteristics in time-lapse movies, the MSER-based automatic cell detection was accompanied by a Kalman filter multiobject tracker that efficiently tracked individual cells even in confluent cell populations. This allowed quantitative cell motion analysis resulting in accurate measurements of the migration magnitude and direction of individual cells, as well as characteristics of collective migration of cell groups. Our results demonstrate that MSER accompanied by temporal data association is a powerful tool for accurate and reliable analysis of the dynamic behaviour of cells in phase-contrast image sequences. These techniques tolerate varying and nonoptimal imaging conditions and due to their relatively light computational requirements they should help to resolve problems in computationally demanding and often time-consuming large-scale dynamical analysis of cultured cells.

Super-Resolution Imaging of Bacteria in a Microfluidics Device

Bacteria have evolved complex, highly-coordinated, multi-component cellular engines to achieve high degrees of efficiency, accuracy, adaptability, and redundancy. Super-resolution fluorescence microscopy methods are ideally suited to investigate the internal composition, architecture, and dynamics of molecular machines and large cellular complexes. These techniques require the long-term stability of samples, high signal-to-noise-ratios, low chromatic aberrations and surface flatness, conditions difficult to meet with traditional immobilization methods. We present a method in which cells are functionalized to a microfluidics device and fluorophores are injected and imaged sequentially. This method has several advantages, as it permits the long-term immobilization of cells and proper correction of drift, avoids chromatic aberrations caused by the use of different filter sets, and allows for the flat immobilization of cells on the surface. In addition, we show that different surface chemistries can be used to image bacteria at different time-scales, and we introduce an automated cell detection and image analysis procedure that can be used to obtain cell-to-cell, single-molecule localization and dynamic heterogeneity as well as average properties at the super-resolution level.

Comparison of Manual and Automated Endothelial Cell Density Analysis in Normal Eyes and DSEK Eyes

To compare automated endothelial cell density analysis with manual cell detection methods with 3 imaging devices. In this prospective study, the corneal endothelium of 54 Descemet stripping endothelial keratoplasty (DSEK) eyes and 28 normal eyes was analyzed with a Nidek Confoscan 4 confocal microscope using a 20× noncontact lens and with Tomey EM-3000 and Konan Noncon Robo SP-8800 specular microscopes. Testing order was randomized. The Confoscan and Robo images were presented in a blinded fashion to an experienced technician for manual cell identification and analysis using the manufacturer's software. A different operator determined endothelial cell density using fully automated software associated with each imaging device. Agreement between methods was assessed by repeated-measures analysis of variance and post hoc Tukey analysis. Manual cell identification on Robo and Confoscan 4 images produced comparable cell density measurements in normal eyes (P = 0.73) and DSEK eyes (P = 0.11). The Confoscan automated cell detection software differed significantly from manual cell detection in both normal and DSEK eyes (P = 0.0003 and P < 0.0001, respectively). The Robo automated cell detection software produced results comparable with manual cell detection in normal eyes (P = 0.082) but significantly overestimated cell density in DSEK eyes (P < 0.0001). The EM-3000 automated cell detection produced results comparable with manual cell detection in normal eyes (P = 0.067) and DSEK eyes (P = 0.49). Only 1 of 3 automated cell detection programs produced cell density readings comparable with those obtained with manual cell identification; the other 2 automated programs significantly overstated endothelial cell density in DSEK eyes.

White Blood Cell Segmentation by Circle Detection Using Electromagnetism-Like Optimization

Medical imaging is a relevant field of application of image processing algorithms. In particular, the analysis of white blood cell (WBC) images has engaged researchers from fields of medicine and computer vision alike. Since WBCs can be approximated by a quasicircular form, a circular detector algorithm may be successfully applied. This paper presents an algorithm for the automatic detection of white blood cells embedded into complicated and cluttered smear images that considers the complete process as a circle detection problem. The approach is based on a nature-inspired technique called the electromagnetism-like optimization (EMO) algorithm which is a heuristic method that follows electromagnetism principles for solving complex optimization problems. The proposed approach uses an objective function which measures the resemblance of a candidate circle to an actual WBC. Guided by the values of such objective function, the set of encoded candidate circles are evolved by using EMO, so that they can fit into the actual blood cells contained in the edge map of the image. Experimental results from blood cell images with a varying range of complexity are included to validate the efficiency of the proposed technique regarding detection, robustness, and stability.

An Improved Computer Vision Method for White Blood Cells Detection

The automatic detection of white blood cells (WBCs) still remains as an unsolved issue in medical imaging. The analysis of WBC images has engaged researchers from fields of medicine and computer vision alike. Since WBC can be approximated by an ellipsoid form, an ellipse detector algorithm may be successfully applied in order to recognize such elements. This paper presents an algorithm for the automatic detection of WBC embedded in complicated and cluttered smear images that considers the complete process as a multiellipse detection problem. The approach, which is based on the differential evolution (DE) algorithm, transforms the detection task into an optimization problem whose individuals represent candidate ellipses. An objective function evaluates if such candidate ellipses are actually present in the edge map of the smear image. Guided by the values of such function, the set of encoded candidate ellipses (individuals) are evolved using the DE algorithm so that they can fit into the WBCs which are enclosed within the edge map of the smear image. Experimental results from white blood cell images with a varying range of complexity are included to validate the efficiency of the proposed technique in terms of its accuracy and robustness.

Mitosis detection in breast cancer histological images An ICPR 2012 contest

Introduction: In the framework of the Cognitive Microscope (MICO) project, we have set up a contest about mitosis detection in images of H and E stained slides of breast cancer for the conference ICPR 2012. Mitotic count is an important parameter for the prognosis of breast cancer. However, mitosis detection in digital histopathology is a challenging problem that needs a deeper study. Indeed, mitosis detection is difficult because mitosis are small objects with a large variety of shapes, and they can thus be easily confused with some other objects or artefacts present in the image. We added a further dimension to the contest by using two different slide scanners having different resolutions and producing red-green-blue (RGB) images, and a multi-spectral microscope producing images in 10 different spectral bands and 17 layers Z-stack. 17 teams participated in the study and the best team achieved a recall rate of 0.7 and precision of 0.89. Context: Several studies on automatic tools to process digitized slides have been reported focusing mainly on nuclei or tubule detection. Mitosis detection is a challenging problem that has not yet been addressed well in the literature. Aims: Mitotic count is an important parameter in breast cancer grading as it gives an evaluation of the aggressiveness of the tumor. However, consistency, reproducibility and agreement on mitotic count for the same slide can vary largely among pathologists. An automatic tool for this task may help for reaching a better consistency, and at the same time reducing the burden of this demanding task for the pathologists. Subjects and Methods: Professor Frédérique Capron team of the pathology department at Pitié-Salpêtrière Hospital in Paris, France, has selected a set of five slides of breast cancer. The slides are stained with H and E. They have been scanned by three different equipments:Aperio ScanScope XT slide scanner, Hamamatsu NanoZoomer 2.0-HT slide scanner and 10 bands multispectral microscope. The data set is made up of 50 high power fields (HPF) coming from 5 different slides scanned at ×40 magnification. There are 10 HPFs/slide. The pathologist has annotated all the mitotic cells manually. A HPF has a size of 512 µm × 512 µm (that is an area of 0.262 mm2 which is a surface equivalent to that of a microscope field diameter of 0.58 mm. These 50 HPFs contain a total of 326 mitotic cells on images of both scanners, and 322 mitotic cells on the multispectral microscope. Results: Up to 129 teams have registered to the contest. However, only 17 teams submitted their detection of mitotic cells. The performance of the best team is very promising, with F-measure as high as 0.78. However, the database we provided is by far too small for a good assessment of reliability and robustness of the proposed algorithms. Conclusions: Mitotic count is an important criterion in the grading of many types of cancers, however, very little research has been made on automatic mitotic cell detection, mainly because of a lack of available data. A main objective of this contest was to propose a database of mitotic cells on digitized breast cancer histopathology slides to initiate works on automated mitotic cell detection. In the future, we would like to extend this database to have much more images from different patients and also for different types of cancers. In addition, mitotic cells should be annotated by several pathologists to reflect the partial agreement among them.

1A2-O07 画像処理による1滴1細胞プリンティングの自動化に関する研究(ナノ・マイクロ流体システム)

Automation of piezoelectric inkjet-based single cell printing is the target of this study. Piezoelectric inkjet technology capability of high speed printing is the main advantage of this technology over other technologies. Therefore, this technology has gained attention in recent field of tissue engineering. Additionally, this technology was successfully developed for single cell handling. However, automation system for the single cell handling was not yet developed. In this study, utilizing open source image processing library, OpenCV, to process image of the inkjet head, automatic cell detection system was successfully developed. Finally, combining cell detection system and cell printing system, automation of single cell printing system with 98% successful ratio was successfully developed.

Automatic Brain Tumor Detection and Isolation of Tumor Cells from MRI Images

Brain Tumor Detection refers to the problem of delineating tumorous tissues from MRI images for the purpose of medical diagnosis and surgical planning. The process uses tumor characteristics in images, such as sizes, shapes, locations and intensities for the isolation of the tumor which depends on manual tracing by experts. This paper proposes automatic brain tumor detection and isolation of tumor cells from MRI images using a genetic algorithm (GA) based clustering method, intensity based asymmetric map and region growing technique.

A graph-mining algorithm for automatic detection and counting of embryonic stem cells in fluorescence microscopy images

Many cell-based research studies require the counting of cells in order to understand and validate experiments through statistical analyses. Although progress in imaging technology has enabled the automation of cell counting for many different cell t

Continuous analysis of dye-loaded, single cells on a microfluidic chip.

Continuous analysis of two dyes loaded into single mammalian cells using laser-based lysis combined with electrophoretic separation was developed and characterized on microfluidic chips. The devices employed hydrodynamic flow to transport cells to a junction where they were mechanically lysed by a laser-generated cavitation bubble. An electric field then attracted the analyte into a separation channel while the membranous remnants passed through the intersection towards a waste reservoir. Phosphatidylcholine (PC)-supported bilayer membrane coatings (SBMs) provided a weakly negatively charged surface and prevented cell fouling from interfering with device performance. Cell lysis using a picosecond-pulsed laser on-chip did not interfere with concurrent electrophoretic separations. The effect of device parameters on performance was evaluated. A ratio of 2 : 1 was found to be optimal for the focusing-channel : flow-channel width and 3 : 1 for the flow-channel : separation-channel width. Migration times decreased with increased electric field strengths up to 333 V cm(-1), at which point the field strength was sufficient to move unlysed cells and cellular debris into the electrophoretic channel. The migration time and full width half-maximum (FWHM) of the peaks were independent of cell velocity for velocities between 0.03 and 0.3 mm s(-1). Separation performance was independent of the exact lysis location when lysis was performed near the outlet of the focusing channel. The migration time for cell-derived fluorescein and fluorescein carboxylate was reproducible with <10% RSD. Automated cell detection and lysis were required to reduce peak FWHM variability to 30% RSD. A maximum throughput of 30 cells min(-1) was achieved. Device stability was demonstrated by analyzing 600 single cells over a 2 h time span.

CellViCAM—Cell viability classification for animal cell cultures using dark field micrographs

Online monitoring of cell density and cell viability is a challenging but essential task to control and optimize biotechnical processes and is of particular interest for the growing field of animal cell cultures. For this purpose, we introduce an optical approach for automated cell detection and viability classification of suspended mammalian cells. Our proposed system CellViCAM is capable of evaluating dark field micrographs by means of several image processing and supervised machine learning techniques without the use of any dyes or fluorescent labeling. Using a human cell line as the reference culture, an efficient cell detection procedure has been established also enabling a cell density estimation. Furthermore, a comprehensive but reagent-free viability analysis, based on a semi-automatic training data generation, has been developed. By means of an extensive validation dataset we can show that the CellViCAM approach can be considered as an equivalent to staining-based methods and moreover, how it provides a technical platform for a more differentiated cell state classification into living, necrotic, early and late apoptosis.

Automated Arabidopsis plant root cell segmentation based on SVM classification and region merging

To obtain development information of individual plant cells, it is necessary to perform in vivo imaging of the specimen under study, through time-lapse confocal microscopy. Automation of cell detection/marking process is important to provide research tools in order to ease the search for special events, such as cell division. In this paper we discuss an automatic cell detection approach for Arabidopsis thaliana based on segmentation, which selects the best cell candidates from a starting watershed-based image segmentation and improves the result by merging adjacent regions. The selection of individual cells is obtained using a support vector machine (SVM) classifier, based on a cell descriptor constructed from the shape and edge strength of the cells' contour. In addition we proposed a novel cell merging criterion based on edge strength along the line that connects adjacent cells' centroids, which is a valuable tool in the reduction of cell over-segmentation. The result is largely pruned of badly segmented and over-segmented cells, thus facilitating the study of cells. When comparing the results after merging with the basic watershed segmentation, we obtain 1.5% better coverage (increase in F-measure) and up to 27% better precision in correct cell segmentation.

Flow and image cytometry side by side for the new frontiers in quantitative single‐cell analysis

THE role of regulatory (CD4CD25) T-cells (Tregs) as therapeutics to replace common anti-inflammatory drugs is of increasing interest since cell therapy seems to be within reach (1). Practical aspects of purification and propagation of Tregs is now summarized by Trzonkowski et al. in the present issue (2). The authors demonstrate that novel advances in flow cytometry and also cellular immunology opens the door for the clinical application of Tregs. Affordable diagnostic tools for HIV and AIDS are of imminent importance in resource poor countries (3). To this end, PCR (4) and cell-based analysis (i.e. CD4 counting) by flow cytometry (5,6) or automated microscopic cytometry (7) is often the method of choice. The current PCR assays, however, often fail to detect virus variants that are prevalent in the third world. Thereby, these patients, despite clinical symptoms, may be diagnosed as virus negative and will not receive therapy. Now, Greve et al. present a new microparticle-based PCR assay that enables detection and quantification of HIV-1 viral load for most of the common variants including outlier, nonmajor, and circulating recombinant forms (8, commentary in 9). Malaria is also one of the great killers that needs appropriate diagnostic tools (7). Jimenez-Diaz et al. (10) developed an assay to quantify malaria-infected erythrocytes using the cell-permeable nuclear dye SYTO-16. The authors show the applicability of their assay for infected normal mice and human erythrocyte engrafted NOD mice. Because flow cytometers are now available in many resource poor settings, both assays can be introduced, relatively inexpensively. Finally, the research group of Edwards et al. applied a flow-cytometry-based high-throughput screening system (11) to identify novel formylpeptide receptor ligand probes. The authors screened more than 24,000 small molecules and finally came up with several candidates of potential future therapeutic or diagnostic interest (12,13). The aforementioned scientific articles of this issue illustrate that flow cytometry is still going strong with innovative technologies for versatile applications presented in our journal. However, every scientific journal has to evolve and adjust its focus to present and adjust for future requirements of the scientific community. In view of the increasing technological advances in imaging (14), our goal was to become a front journal for image cytometry and quantitative image analysis. As one can see from the citations in 2008 to Cytometry Part A, the readers find an increasing appreciation in our publications in the technological field of quantitative imaging. As one good example, Abella et al. (15) present an assay for automated quantification of cell infiltrates in histological sections based on microscopic images from allergic murine models. After extraction of texture parameters by a stepwise procedure the authors propose an algorithm that appears to be superior to manual cell counting. Yet another application is automated laser scanning cytometry (LSC) for mast cells tracking in skin as described by Zoog et al. (16). Rapid, accurate evaluation of mast cells present around wounds in a wound-healing model by automated LSC may serve as an important tool for tracking pharmacodynamic effects of MC-directed therapies. Without doubt we enter the era of automated cell detection in tissues (17). New labeling fluorescent tools for life-cell in vivo imaging are introduced by Robers et al. (18). High-affinity and selective ligand–receptor binding of 50-fluorescein-tagged synthetic ligand for FKBP12 (immunophilin) and mutated receptor

Multiclass cell detection in bright field images of cell mixtures with ECOC probability estimation

To achieve high throughput with robotic systems based on optical microscopy, it is necessary to replace the human observer with computer vision algorithms that can identify and localize individual cells as well as carry out additional studies on these cells in relation to biochemical parameters. The latter task is best accomplished with the use of fluorescent probes. Since the number of fluorescence channels is limited, it is highly desirable to accomplish the cell identification and localization task with transmitted light microscopy. In previous work, we developed algorithms for automatic detection of unstained cells of a single type in bright field images [X. Long, W.L. Cleveland, Y.L. Yao, A new preprocessing approach for cell recognition, IEEE Transactions on Information Technology in Biomedicine 9 (3) (2005) 407–412; X. Long, W.L. Cleveland, Y.L. Yao, Automatic detection of unstained viable cells in bright field images using a support vector machine with an improved training procedure, Computers in Biology and Medicine 36 (2006) 339–362]. Here we extend this technology to facilitate identification and localization of multiple cell types. We formulate the detection of multiple cell types in mixtures as a supervised, multiclass pattern recognition problem and solve it by extension of the Error Correcting Output Coding (ECOC) method to enable probability estimation. The use of probability estimation provides both cell type identification as well as cell localization relative to pixel coordinates. Our approach has been systematically studied under different overlap conditions and outperforms several commonly used methods, primarily due to the reduction of inconsistent labeling by introducing redundancy. Its speed and accuracy are sufficient for use in some practical systems.

MPScope: A versatile software suite for multiphoton microscopy

MPScope is a software suite to control and analyze data from custom-built multiphoton laser scanning fluorescence microscopes. The acquisition program MPScan acquires, displays and stores movies, linescans, image stacks or arbitrary regions from up to four imaging channels and up to two analog inputs, while plotting the intensity of regions of interest in real-time. Bidirectional linescans allow 256 × 256 pixel frames to be acquired at up to 10 fps with typical galvanometric scanners. A fast stack mode combines movie acquisition with continuous z-focus motion and adjustment of laser intensity for constant image brightness. Fast stacks can be automated by custom programs running in an integrated scripting environment, allowing a 1 mm 3 cortical volume to be sampled in 1 billion voxels in approximately 1 h. The analysis program MPView allows viewing of stored frames, projections, automatic detection of cells and plotting of their average intensity across frames, direct frame transfer to Matlab, AVI movie creation and file export to ImageJ. The combination of optimized code, multithreading and COM (Common Object Model) technologies enables MPScope to fully take advantage of custom-built two-photon microscopes and to simplify their realization.