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 Conclusion


Figure 9 : Comparison of volume-based metrics of microvascular networks in the forebrain cortex and cerebellum of a mouse. Cerebellum is shown in red, forebrain neocortex shown in blue. (A) Z -depth comparison of vessel density (number of vessels per mm 2 ); each face depth value is equivalent to examining the density for a single 2D image. (B) Distribution of surface-area-to-volume ratio over all faces in the sample volume. (C) Distribution of vessel density over all 2D faces in the sample volumes, obtained by fl attening the 2D values from A into a histogram. (D) Distribution of segment tortuosity from vectorized vessel traces.


transformations required to reconstruct traditional whole- slide images into 3D volumetric views. Workfl ow effi ciency . By slicing and scanning in one step, the KESM automated imaging system is a redesign of the microscopy workfl ow that provides many advantages over traditional microtome and imaging methods. In addition to replacing manual glass slide creation and storage, it reduces the time needed to collect large volumes of data at submicron resolution. Downstream processing of data is more effi cient because the images need minimal registration to create a volume image.


Tissue quantifi cation . T e KESM signifi cantly improves


the quantifi cation of microvasculature imaging in comparison to estimations made from 2D images collected from traditional histological techniques, which are limited by their smaller viewable area and minimal depth of view. In examining blood vessels, a structure that is 3D but oſt en analyzed in 2D, we found little standardization of metrics across the fi eld, making comparison to prior methods challenging [ 13 ]. By comparing volumetric and vectorized measurements of mouse cerebellum and forebrain, signifi cant diff erences in vascular density, surface area to volume ratio, and vessel tortuosity were discovered. In addition, each region showed signifi cant diff erences in quanti- fi cation of features compared to 2D images. T is suggests that subsampling of tissue may introduce a bias in stereological estimates.


20


Serial section microscopy is diffi cult to achieve on organ scale because of the manual nature of sectioning, scanning, and reconstructing images from 2D slides. T e KESM is the centerpiece of a 3D pathology platform designed to solve problems currently inhibiting the widespread application of serial section microscopy by automating the process of 3D tissue imaging. T e KESM data can be used by researchers to quickly gather and analyze high-precision 3D images. Quantifying biological diff er- ences within an organ, or between organisms, is especially useful for animal models used in pre-clinical drug discovery research [ 14 ]. T is enables quantifi cation of microvas- culature and may provide insight into various diseases, such as Alzheimer’s, diabetes, and cancer. By examining large volumes of tissue at maximum light microscopy resolution with high throughput, scientists and pathologists can improve the accuracy of morpho- logical assessment and identifi cation of normal and diseased tissue states.


References [1] JR Chung et al ., Frontiers in Neuroinformatics 5 ( 2011 ) 29 . [2] ME Vandenberghe et al ., Scientifi c Reports 6 ( 2016 ) 20958 .


[3] D Mayerich et al ., J Microsc 231 ( 1 ) (2008 ) 134 – 43 . [4] AX Falcão et al ., IEEE T Pattern Anal 26 ( 1 ) (2004 ) 19 – 29 . [5] AF Frangi et al ., Medical Image Computing and Computer-Assisted Intervention - MICCAI’98 (Vol. 1496) , Springer , Berlin Heidelberg , 1998, 130 –3 7 .


[6] D Mayerich and J Keyser , IEEE T Vis Comput Gr 15 ( 4 ) (2009 ) 670 – 81 .


[7] PS Tsai et al ., J Neurosci 29 ( 46 ) (2009 ) 14553 – 70 . [8] K Palágyi and A Kuba , Graph Model Im Proc 61 ( 4 ) (1999 ) 199 – 221 .


[9] VNP Vemuri , Feature-Based Analysis of Microvasculature in High Resolution Microscopy Images of Mice Brains , University of Houston , Houston , 2016 .


[10] T Wang and I Cheng , Lecture Notes in Computer Science (Vol. 5358 LNCS), Springer , Berlin Heidelberg , 2008 , 1051 – 60 .


[11] R Scorcioni et al ., Nature Protocols 3 ( 5 ) (2008 ) 866 – 76 . [12] F Cassot et al ., Microcirculation 13 ( 1 ) (2006 ) 1 – 18 . [ 13] WY Leung and MB Jensen , ISRN Neurology (2013) vol 2013: 853737 .


[14] H Kobayashi et al ., Int J Cancer 112 ( 6 ) (2004 ) 920 – 26 . www.microscopy-today.com • 2017 July


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