Imaris Software For Mac Os X [UPDATED]
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LAS X Core: Free software from Leica to open images acquired on the Leica SP8 confocal or any microscope controlled by LAS X. Scroll down the linked page to find the version appropriate to your operating system. Windows only.
For a free copy of ZEN lite image processing software, click here: ZEN lite (Windows only). The HCBI recommends frequent downloads of ZEN to ensure your software is up-to-date and compatible with files from our newest microscopes
Click here to view six 30-minute indroductory videos to the Vision4D software.Click here to read several use case and case study documents.Click here to watch several workshop and webinar videos.
Acquisition [top] Hamamatsu Orca 12-bit Camera Shading Corrector QuickTime Capture (Capture images using QuickTime) TWAIN JTwain Twain Scan SensiCam Long Exposure Camera Video Capture Macro Tool(Video for Windows via VirtualDub) Capturing plugin(Captures images on Windows using JMF) Webcam Capture (Video capture on OS X, Linux and Windows) www.qimaging.com:QImaging Firewire Cameras ScionFGAkiz:Scion full-frame-rate capture FWCamAkiz:Mac OS X Firewire Cameras www.pixelsmart.com:PixelSmart Frame Grabbers www.bruxton.com:Andor, Cooke, Hamamatsu, PCO, Princeton Instruments, Photometrics, Red Shirt Imaging and SciMeasure Cameras www.aas2.com:Ann Arbor Sensor Systems AXT100 Thermal Imaging Camera www.pco.de:Cooke PCO, Sensicam and Pixelfly Cameras mbl.edu:CamAcqJ plugin for QImaging Retiga cameras (Windows only) www.fclab.com:FCLabFC1000/2000 USB 2.0 Cameras (Windows only) micro-manager.org(μManager): Open source, multi-platform, extendable; stage, filter wheel and shutter control; serial I/O; Zeiss and Nikon microscopes; Hamamatsu, Andor, PVCAM, DVC and IIDC Firewire cameras; Shutters, stages, etc. by Vincent (Uniblitz), Ludl, Prior, ASI and Sutter PHASE GmbH:Firewire and GigE Vision camera control software (Windows only) CivilCapture:Capture images using theLTI-Civil Java library Lumenera:Infinity USB 2.0 cameras (Mac only) Dage-MTI:Plugin for XLV, XL16 and XLM cameras (Windows only) Jenoptik:Mac and Windows plugins for ProgRes microscope cameras AVerMedia:Plugins for DarkCrystal HD Capture cards (Windows only) iSight Capture: Webcam video capture using JavaCV and OpenCV Videoscan:Plugin for Videoscan camera (Windows only) HF_IDS_Cam:High Frequency IDS Camera Capture (Linux and Windows only)
The increasing number of novel approaches for large-scale, multi-dimensional imaging of cells has created an unprecedented opportunity to analyze plant morphogenesis. However, complex image processing, including identifying specific cells and quantitating parameters, and high running cost of some image analysis softwares remains challenging. Therefore, it is essential to develop an efficient method for identifying plant complex multicellularity in raw micrographs in plants.
In mathematics, the Voronoï diagram (named after Georgy Voronoi), also known as the Dirichlet tessellation (named after Lejeune Dirichlet) or Voronoï tessellation, is a group of contiguous polygons that are closely fitted together in a repeated pattern without gaps or overlaps [22, 23]. The Voronoï diagram, which contains discrete data points connected to a Delaunay triangle network, is a partition of a planar space; this partition is key to establishing the tessellation algorithm . Centroidal Voronoï tessellation is a useful tool with applications in many fields ranging from geography, meteorology, and crystallography to the aerospace industry. This tool analyzes the nearest point in a structure, the minimum closed circle, and many spatial measurements including adjacency, proximity, and accessibility analysis [25,26,27,28]. In cell biology, VoronoÏ tessellations have been used to model the geometric arrangement of cells in morphogenetic or cancerous tissues . The open-source SR-Tesseler segmentation software package based on Voronoï tessellation was recently developed for the precise, robust, automatic quantification of protein organization from single-molecule localization microscopy images [30,31,32,33]. SR-Tesseler can also be used to detect cell clustering based on the spatial distribution of cellular centroidal points. In addition, SR-Tesseler can be used to segment a dense multicellular structure by setting the threshold of these polygons at average localization densities, mean distance, and area, making it suitable for analyzing multicellularity in plants.
To verify the results obtained by ImageJ, we analyzed the same raw image with Bitplane Imaris, a powerful software tool for 3/4D image visualization and analysis (Fig. 1e). In addition to segmenting cell outlines with Imaris, we generated a heatmap color-coded according to cell area (Fig. 1f). The total cell numbers and cell areas acquired by ImageJ and Imaris were 5845/6070 and 751,812/855,953 (μm2) (with proportions of 1:1.0385 and 1.1385), respectively (Fig. 1g, h, Additional file 7: Dataset S1). The average cell areas were 117.8473 and 126.3316 (μm2) (with a proportion of 1:1.0720), respectively (Fig. 1i). There were no obvious differences in the frequency distributions of cell areas based on these two results (Fig. 1j). Pearson correlation analysis also suggested that the cell areas were extremely similar based on comparisons of ImageJ/ImageJ, ImageJ/Imaris, and Imaris/Imaris results (Fig. 1k). Although Imaris has a friendly user interface and diverse statistical visualizations, some functions are not free of charge, and it can only export cell area values calculated based on the number of voxels. Consequently, we chose ImageJ and SR-Tesseler, two freely available open-source software packages, to develop an efficient procedure to characterize, segment, and quantify complex multicellularity in plants based on raw microscopy images.
Flowchart of the procedure. a A plant sample (Arabidopsis seedling) prepared for analysis. b Basic raw imaging data for cellular outlines acquired by various 2D (two dimensional) and 3D imaging techniques used for this procedure; large-scale 3D images can be split into arbitrary 2D sections if needed. c Pre-processing, clarity adjustment, and parameter identification by ImageJ software. d Polygon creation, establishment of a Voronoï diagram, and object/cluster identification together with quantitative data generated and exported by SR-Tesseler software
Next, we exported the centroid coordinates obtained from cell particle identification (Additional file 1: Fig. S1a and Additional file 9: Dataset S3); the location of a selected coordinate is shown in Fig. 4a. After converting the data into a .csv file, we performed multicellularity segmentation of the centroid data using SR-Tesseler software. After importing the modified centroid data into SR-Tesseler, three windows appeared, including a console for application messages, a control panel, and a data viewer that displayed the dots of the centroid (Fig. 4b). By merging this information with binarization of the raw image, the centroids of each cell were precisely located (Fig. 4c).
We identified cell centroids with ImageJ and further analyzed them using SR-Tesseler software. This analysis generated a rich set of data about intracellular connections and functional structures, providing a basis for identifying difficult-to-differentiate tissue structures. For instance, we can determine the location and direction of the vascular bundle in roots based on the pseudocolor graph by setting the threshold at localization density when clusters are created. In addition, the level of plant cell communication can be illustrated by the readout of pseudocolor images using the mean distance threshold setup. The distribution of different cell types can also be shown by setting the threshold at area; for example, using this technique, leaf epidermis can be distinguished from stomatal cells and root epidermal cells can be distinguished from cortical cells.
Raw images of cellular outlines acquired from two-dimensional (2D) sections and 3D imaging techniques can be analyzed with this procedure. The format of the input images should be supported by ImageJ software, including tiff, png, gif, jpeg, bmp, gicom, fits files, and the like. Some images produced by various optical microscopy techniques, such as light sheet fluorescence microscopy (LSFM), laser scanning confocal microscopy (LSCM), and so on can be exported to a format supported by ImageJ using software provided with the microscope. Multi-scale 3D images generated by volume electron microscopy, LSFM, or micro/nano computed tomography (Micro/Nano CT) and so on should be split into arbitrary 2D sections with clear cellular outlines from the ROI after reconstruction and prior to analysis.
Imaris software was used to evaluate the veracity of cell identification by ImageJ. The following procedure was used: Import the image from Imaris File Converter or directly drop it into Imaris software (in a supported format). The image is then displayed in the Surpass view. Click on the Add new Cells icon to highlight the cell creation. Choose the last detection types of cells and click on the Next button. Two different detection algorithms can be used, depending on the cell staining technique and sample preparation (cytoplasm or cell membrane boundary). Click the Cell Membrane Detection button and choose the correct source channel of the raw image. Use two consecutive clicks to measure the diameter of the smallest cell and membrane detail before inputting the relevant measuring data and then click the Next button. After adjusting the cell membrane threshold based on intensity and quality, perform cell classification using various types of filters and then click the Finish button. Use the Color icon to define the pseudocolor and the Statistics icon to obtain the detailed cell parameter values. 153554b96e