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


( Figure 9B ). When distributions of volume and surface area are compared directly, the forebrain region shows a signifi cantly lower volume ( Z = −10.24, p < 1e-23) and signifi cantly lower surface area ( Z = −10.10, p < 1e-23) than the cerebellum. In the regions measured, the cerebellum had a large arteriole, which may account for much of the observed diff erence in volume and surface area.


From the vascular network graphs generated from skeletonized volumes (red lines inside volume renderings of Figure 8 C, 8 D), vessel segments are identifi ed as the list of nodes between a branch point (bifurcation point of the graph) and an end point (graph terminus), two branch points, or two end points.


A commonly cited metric for microvasculature is the tortuosity of each vessel, measured as the Euclidean distance between the segment start and end points divided by the full path length of the segment [ 12 ]. A value of 1 indicates that the vessel length is the same as its distance, that is, the path is a straight line between two points. Values much greater than 1 indicate a higher degree of tortuosity, or deviation from the straight line approximation. In the samples examined, the cerebellum had a signifi cantly higher tortuosity than the forebrain (Wilcoxon rank-sum test, Z = 9.37, p < 7e-21) ( Figure 9d ). Future advancements in vessel classifi cation algorithms will increase the accuracy and precision of measurements of the microvascular network.


Discussion T e KESM brings together elements of machine vision, automated serial sectioning, micromachining, advanced optics, and robotics to produce 3D images that can be used to quantify, manipulate, and analyze tissue structures. T e following


Figure 8 : Selection of volumes of interest in whole mouse brain vasculature. (A) 3D rendering of microvasculature from mouse brain left hemisphere. Voxels were downsampled by 16× to create the render; only prominent vessels are shown. (B) Sagittal section of mouse brain, after stitching and cleaning. Boxes indicate the location and size of the subregions sampled: blue = forebrain, red = cerebellum. (C, D) Renderings of the microvasculature (gray) and skeleton (red) of the selected subregions, where (C) is from the forebrain and (D) is from the cerebellum.


paragraphs review some of the advantages of this new method. Imaging scale and resolution . T e KESM fi lls a need for high-resolution imaging at high volume. With the stage limits listed in the Methods section above, this process enables a maximum sample volume of over 100 cm 3 at sub-micron resolution, providing the capacity to image whole murine organs and large sections of higher animal organs. Because up to a terabyte of data is produced per cm 3 tissue, accompanying


2017 July • www.microscopy-today.com


soſt ware facilitates the processing of large swaths of data, allowing quantitative analytics and a wide variety of interactive viewing methods.


Image registration . Because of the precise stage motion in the KESM, tissue sectioned from the block can be concat- enated into 3D images through alignment of the voxels in the digital volume. T is removes the impediment of computationally expensive and imperfect image registration


19


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