Cargando…

Imalytics Preclinical: Interactive Analysis of Biomedical Volume Data

A software tool is presented for interactive segmentation of volumetric medical data sets. To allow interactive processing of large data sets, segmentation operations, and rendering are GPU-accelerated. Special adjustments are provided to overcome GPU-imposed constraints such as limited memory and h...

Descripción completa

Detalles Bibliográficos
Autores principales: Gremse, Felix, Stärk, Marius, Ehling, Josef, Menzel, Jan Robert, Lammers, Twan, Kiessling, Fabian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Ivyspring International Publisher 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4737721/
https://www.ncbi.nlm.nih.gov/pubmed/26909109
http://dx.doi.org/10.7150/thno.13624
_version_ 1782413510039306240
author Gremse, Felix
Stärk, Marius
Ehling, Josef
Menzel, Jan Robert
Lammers, Twan
Kiessling, Fabian
author_facet Gremse, Felix
Stärk, Marius
Ehling, Josef
Menzel, Jan Robert
Lammers, Twan
Kiessling, Fabian
author_sort Gremse, Felix
collection PubMed
description A software tool is presented for interactive segmentation of volumetric medical data sets. To allow interactive processing of large data sets, segmentation operations, and rendering are GPU-accelerated. Special adjustments are provided to overcome GPU-imposed constraints such as limited memory and host-device bandwidth. A general and efficient undo/redo mechanism is implemented using GPU-accelerated compression of the multiclass segmentation state. A broadly applicable set of interactive segmentation operations is provided which can be combined to solve the quantification task of many types of imaging studies. A fully GPU-accelerated ray casting method for multiclass segmentation rendering is implemented which is well-balanced with respect to delay, frame rate, worst-case memory consumption, scalability, and image quality. Performance of segmentation operations and rendering are measured using high-resolution example data sets showing that GPU-acceleration greatly improves the performance. Compared to a reference marching cubes implementation, the rendering was found to be superior with respect to rendering delay and worst-case memory consumption while providing sufficiently high frame rates for interactive visualization and comparable image quality. The fast interactive segmentation operations and the accurate rendering make our tool particularly suitable for efficient analysis of multimodal image data sets which arise in large amounts in preclinical imaging studies.
format Online
Article
Text
id pubmed-4737721
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher Ivyspring International Publisher
record_format MEDLINE/PubMed
spelling pubmed-47377212016-02-23 Imalytics Preclinical: Interactive Analysis of Biomedical Volume Data Gremse, Felix Stärk, Marius Ehling, Josef Menzel, Jan Robert Lammers, Twan Kiessling, Fabian Theranostics Research Paper A software tool is presented for interactive segmentation of volumetric medical data sets. To allow interactive processing of large data sets, segmentation operations, and rendering are GPU-accelerated. Special adjustments are provided to overcome GPU-imposed constraints such as limited memory and host-device bandwidth. A general and efficient undo/redo mechanism is implemented using GPU-accelerated compression of the multiclass segmentation state. A broadly applicable set of interactive segmentation operations is provided which can be combined to solve the quantification task of many types of imaging studies. A fully GPU-accelerated ray casting method for multiclass segmentation rendering is implemented which is well-balanced with respect to delay, frame rate, worst-case memory consumption, scalability, and image quality. Performance of segmentation operations and rendering are measured using high-resolution example data sets showing that GPU-acceleration greatly improves the performance. Compared to a reference marching cubes implementation, the rendering was found to be superior with respect to rendering delay and worst-case memory consumption while providing sufficiently high frame rates for interactive visualization and comparable image quality. The fast interactive segmentation operations and the accurate rendering make our tool particularly suitable for efficient analysis of multimodal image data sets which arise in large amounts in preclinical imaging studies. Ivyspring International Publisher 2016-01-01 /pmc/articles/PMC4737721/ /pubmed/26909109 http://dx.doi.org/10.7150/thno.13624 Text en © Ivyspring International Publisher. Reproduction is permitted for personal, noncommercial use, provided that the article is in whole, unmodified, and properly cited. See http://ivyspring.com/terms for terms and conditions.
spellingShingle Research Paper
Gremse, Felix
Stärk, Marius
Ehling, Josef
Menzel, Jan Robert
Lammers, Twan
Kiessling, Fabian
Imalytics Preclinical: Interactive Analysis of Biomedical Volume Data
title Imalytics Preclinical: Interactive Analysis of Biomedical Volume Data
title_full Imalytics Preclinical: Interactive Analysis of Biomedical Volume Data
title_fullStr Imalytics Preclinical: Interactive Analysis of Biomedical Volume Data
title_full_unstemmed Imalytics Preclinical: Interactive Analysis of Biomedical Volume Data
title_short Imalytics Preclinical: Interactive Analysis of Biomedical Volume Data
title_sort imalytics preclinical: interactive analysis of biomedical volume data
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4737721/
https://www.ncbi.nlm.nih.gov/pubmed/26909109
http://dx.doi.org/10.7150/thno.13624
work_keys_str_mv AT gremsefelix imalyticspreclinicalinteractiveanalysisofbiomedicalvolumedata
AT starkmarius imalyticspreclinicalinteractiveanalysisofbiomedicalvolumedata
AT ehlingjosef imalyticspreclinicalinteractiveanalysisofbiomedicalvolumedata
AT menzeljanrobert imalyticspreclinicalinteractiveanalysisofbiomedicalvolumedata
AT lammerstwan imalyticspreclinicalinteractiveanalysisofbiomedicalvolumedata
AT kiesslingfabian imalyticspreclinicalinteractiveanalysisofbiomedicalvolumedata