Graphical processing unit implementation of an integrated shape-based active contour: Application to digital pathology

Commodity graphics hardware has become a cost-effective parallel platform to solve many general computational problems. In medical imaging and more so in digital pathology, segmentation of multiple structures on high-resolution images, is often a complex and computationally expensive task. Shape-bas...

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Vydané v:Journal of pathology informatics Ročník 2; číslo 2; s. 13
Hlavní autori: Ali, Sahirzeeshan, Madabhushi, Anant
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: India Elsevier Inc 01.01.2012
Medknow Publications and Media Pvt. Ltd
Medknow Publications & Media Pvt Ltd
Elsevier
Predmet:
Digital Pathology
Fast Active Contour
GPU Implementation
Histopathology
Level set
Medical imaging
Medical imaging equipment
Multi-threaded programming
Parallel Processing
Segmentation
Symposium - Original Research
Medical imaging
Histopathology
Level set
Segmentation
GPU Implementation
Parallel Processing
Multi-threaded programming
Fast Active Contour
Digital Pathology
ISSN:2153-3539, 2229-5089, 2153-3539
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Shrnutí:Commodity graphics hardware has become a cost-effective parallel platform to solve many general computational problems. In medical imaging and more so in digital pathology, segmentation of multiple structures on high-resolution images, is often a complex and computationally expensive task. Shape-based level set segmentation has recently emerged as a natural solution to segmenting overlapping and occluded objects. However the flexibility of the level set method has traditionally resulted in long computation times and therefore might have limited clinical utility. The processing times even for moderately sized images could run into several hours of computation time. Hence there is a clear need to accelerate these segmentations schemes. In this paper, we present a parallel implementation of a computationally heavy segmentation scheme on a graphical processing unit (GPU). The segmentation scheme incorporates level sets with shape priors to segment multiple overlapping nuclei from very large digital pathology images. We report a speedup of 19× compared to multithreaded C and MATLAB-based implementations of the same scheme, albeit with slight reduction in accuracy. Our GPU-based segmentation scheme was rigorously and quantitatively evaluated for the problem of nuclei segmentation and overlap resolution on digitized histopathology images corresponding to breast and prostate biopsy tissue specimens.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:2153-3539
2229-5089
2153-3539
DOI:10.4103/2153-3539.92029