GPU-accelerated brain connectivity reconstruction and visualization in large-scale electron micrographs

Handle URI:
http://hdl.handle.net/10754/575840
Title:
GPU-accelerated brain connectivity reconstruction and visualization in large-scale electron micrographs
Authors:
Jeong, Wonki; Pfister, Hanspeter; Beyer, Johanna; Hadwiger, Markus ( 0000-0003-1239-4871 )
Abstract:
This chapter introduces a GPU-accelerated interactive, semiautomatic axon segmentation and visualization system. Two challenging problems have been addressed: the interactive 3D axon segmentation and the interactive 3D image filtering and rendering of implicit surfaces. The reconstruction of neural connections to understand the function of the brain is an emerging and active research area in neuroscience. With the advent of high-resolution scanning technologies, such as 3D light microscopy and electron microscopy (EM), reconstruction of complex 3D neural circuits from large volumes of neural tissues has become feasible. Among them, only EM data can provide sufficient resolution to identify synapses and to resolve extremely narrow neural processes. These high-resolution, large-scale datasets pose challenging problems, for example, how to process and manipulate large datasets to extract scientifically meaningful information using a compact representation in a reasonable processing time. The running time of the multiphase level set segmentation method has been measured on the CPU and GPU. The CPU version is implemented using the ITK image class and the ITK distance transform filter. The numerical part of the CPU implementation is similar to the GPU implementation for fair comparison. The main focus of this chapter is introducing the GPU algorithms and their implementation details, which are the core components of the interactive segmentation and visualization system. © 2011 Copyright © 2011 NVIDIA Corporation and Wen-mei W. Hwu Published by Elsevier Inc. All rights reserved..
KAUST Department:
Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program
Publisher:
Elsevier BV
Journal:
GPU Computing Gems Emerald Edition
Issue Date:
2011
DOI:
10.1016/B978-0-12-384988-5.00049-8
Type:
Book Chapter
ISBN:
9780123849885
Appears in Collections:
Computer Science Program; Visual Computing Center (VCC); Book Chapters; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorJeong, Wonkien
dc.contributor.authorPfister, Hanspeteren
dc.contributor.authorBeyer, Johannaen
dc.contributor.authorHadwiger, Markusen
dc.date.accessioned2015-08-24T09:55:01Zen
dc.date.available2015-08-24T09:55:01Zen
dc.date.issued2011en
dc.identifier.isbn9780123849885en
dc.identifier.doi10.1016/B978-0-12-384988-5.00049-8en
dc.identifier.urihttp://hdl.handle.net/10754/575840en
dc.description.abstractThis chapter introduces a GPU-accelerated interactive, semiautomatic axon segmentation and visualization system. Two challenging problems have been addressed: the interactive 3D axon segmentation and the interactive 3D image filtering and rendering of implicit surfaces. The reconstruction of neural connections to understand the function of the brain is an emerging and active research area in neuroscience. With the advent of high-resolution scanning technologies, such as 3D light microscopy and electron microscopy (EM), reconstruction of complex 3D neural circuits from large volumes of neural tissues has become feasible. Among them, only EM data can provide sufficient resolution to identify synapses and to resolve extremely narrow neural processes. These high-resolution, large-scale datasets pose challenging problems, for example, how to process and manipulate large datasets to extract scientifically meaningful information using a compact representation in a reasonable processing time. The running time of the multiphase level set segmentation method has been measured on the CPU and GPU. The CPU version is implemented using the ITK image class and the ITK distance transform filter. The numerical part of the CPU implementation is similar to the GPU implementation for fair comparison. The main focus of this chapter is introducing the GPU algorithms and their implementation details, which are the core components of the interactive segmentation and visualization system. © 2011 Copyright © 2011 NVIDIA Corporation and Wen-mei W. Hwu Published by Elsevier Inc. All rights reserved..en
dc.publisherElsevier BVen
dc.titleGPU-accelerated brain connectivity reconstruction and visualization in large-scale electron micrographsen
dc.typeBook Chapteren
dc.contributor.departmentVisual Computing Center (VCC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.identifier.journalGPU Computing Gems Emerald Editionen
dc.contributor.institutionHarvard University, United Statesen
kaust.authorBeyer, Johannaen
kaust.authorHadwiger, Markusen
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