search.noResults

search.searching

note.createNoteMessage

search.noResults

search.searching

orderForm.title

orderForm.productCode
orderForm.description
orderForm.quantity
orderForm.itemPrice
orderForm.price
orderForm.totalPrice
orderForm.deliveryDetails.billingAddress
orderForm.deliveryDetails.deliveryAddress
orderForm.noItems
Knife-Edge Scanning Microscopy


viewing and processing. Eventually the entire dataset is sent to Amazon Web Services (AWS) ( Figure 5A ). Database operations . T e KESM


soſt ware operates the local processing and storage clusters in a decentralized manner to prevent single points of failure and to facilitate incremental improvement. T e database ( Figure 5B ) serves several critical functions: (a) storing image metadata, (b) relaying commands to the KESM from the user interface or from programmatic manipulation of the database, (c) synchronizing computa- tional processing via interlock checks for completed tasks, and (d) providing data for the user interface servers. Image analysis . Once the slice images from the microscope are stored, they must be stitched together to create a large 3D volume representing the digitized tissue. Fidelity of the reconstruction requires multiple image processing steps such as cleaning and fi ltering, as well as later steps that compress the data into object classifi - cation and object metrics. T e scale of data compression spans from trillions of bytes down to a single byte that can


Figure 6 : Cropped image slice and full-resolution zoom of mouse kidney with India ink localized vasculature. Raw images are shown as acquired from the KESM; no post-processing was done. The larger image shows a cropped slice of 12,500 × 4,096 pixels or 50 megapixels. The usable portion of the slice is restricted to the fi eld of view of the objective, or about 2 mm wide for the objective lenses used and is 2,858 pixels wide per slice. The black stripe is the un-illuminated edge of the knife. Original resolution for both images was 0.7 × 0.7 µm/pixel.


represent a single measurement from an entire organ or other piece of tissue. T e sample slices are stored as narrow strips 4,096 pixels


wide ( Figure 6 ). T e physical limits of the stage restrict our sample size to 50 × 50 × 20 mm. T is translates to 70,000 pixels across or 25 slices wide. With an average slice thickness of 5 µm, the KESM can take up to 4,000 slices across the Z depth. T is translates to a maximum volume of 70,000 × 70,000 × 4,000 pixels or 19.6 teravoxels (Tv) of data. Aſt er initial digital isolation of the tissue of interest, such as the mouse brain examined in this article, the region of interest might be 20,000 × 15,000 × 1,000 pixels (approximately 14 × 10.5 × 5 mm) or 300 gigavoxels (Gv).


A distributed processing framework is necessary to transform imaging data at this scale. T e Apache Spark platform uses a MapReduce framework, which makes it easy to rapidly process terabytes of data across as many CPUs as needed. Amazon’s Elastic Computing Cloud (EC2) allows adaptive scaling of computational infrastructure to match needed processing requirements. Amazon’s Simple Storage Service (S3) serves as our storage solution for all analysis outputs, as well as the original raw slice images. Use of distributed storage and distributed processing with S3 and EC2, respectively, dramati- cally reduces the time to write and read the images from hard disk, one of the largest time bottlenecks in processing data of this magnitude. T e Python 3 programming language is used for all analysis and leverages many available libraries, such as numpy, scipy, and openCV.


2017 July • www.microscopy-today.com


In the fi rst stage of image processing, a cuboid region of interest (ROI) containing the tissue to be examined is identifi ed. Once the ROI has been selected, the raw slices are stitched together ( Figure 7A ). Due to the high precision of the KESM stage motion, no image registration transformations are necessary to align the tissue features; voxels in the digital volume are aligned in correspondence with how the tissue is sectioned from the block. T erefore, slice image arrays can be cropped and concatenated to create a 2D face ( Figure 7B ). Once stitched, standard algorithms are used to clean mechanical and optical artifacts ( Figure 7C ). In the example presented here, blood vessels stained with India ink are the objects of interest and are segmented with one of many available segmentation algorithms [ 4 – 7 ] ( Figure 7D ). Segmented vessels are then thinned to their skeleton or the topological equivalent as a single pixel traces through all branches [ 8 – 10 ]. T is skeleton is then vectorized in a graph, and measurements are taken from this representation. Metrics are calculated from classifi ed vessel segments and branch points [ 11 ] ( Figure 7E ). Finally, the 3D voxel data can be combined to create a 3D rendering of the tissue that allows views of entire organs ( Figure 7F ).


Results


To compare the vascular networks from diff erent areas of a mouse brain ( Figure 8A ), 360 µm cubes (512 × 512) pixels per face, 5 µm slice thickness) from the forebrain neocortex (somatomotor area) and cerebellum were digitally isolated ( Figure 8B ). Volumetric and vectorized measurements were


17


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68  |  Page 69  |  Page 70  |  Page 71  |  Page 72  |  Page 73  |  Page 74  |  Page 75  |  Page 76