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Decreasing time consumption of microscopy image segmentation through parallel processing on the GPU

Joris Roels (UGent) , Jonas De Vylder (UGent) , Yvan Saeys (UGent) , Bart Goossens and Wilfried Philips (UGent)
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Abstract
The computational performance of graphical processing units (GPUs) has improved significantly. Achieving speedup factors of more than 50x compared to single-threaded CPU execution are not uncommon due to parallel processing. This makes their use for high throughput microscopy image analysis very appealing. Unfortunately, GPU programming is not straightforward and requires a lot of programming skills and effort. Additionally, the attainable speedup factor is hard to predict, since it depends on the type of algorithm, input data and the way in which the algorithm is implemented. In this paper, we identify the characteristic algorithm and data-dependent properties that significantly relate to the achievable GPU speedup. We find that the overall GPU speedup depends on three major factors: (1) the coarse-grained parallelism of the algorithm, (2) the size of the data and (3) the computation/memory transfer ratio. This is illustrated on two types of well-known segmentation methods that are extensively used in microscopy image analysis: SLIC superpixels and high-level geometric active contours. In particular, we find that our used geometric active contour segmentation algorithm is very suitable for parallel processing, resulting in acceleration factors of 50x for 0.1 megapixel images and 100x for 10 megapixel images.
Keywords
Microscopy, Image segmentation, GPGPU computing, FEATURES, MODELS

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Citation

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Chicago
Roels, Joris, Jonas De Vylder, Yvan Saeys, Bart Goossens, and Wilfried Philips. 2016. “Decreasing Time Consumption of Microscopy Image Segmentation Through Parallel Processing on the GPU.” In Lecture Notes in Computer Science, ed. J Blanc Talon, C Distante, W Philips, D Popescu, and P Scheunders, 10016:147–159. Cham, Switzerland: Springer.
APA
Roels, Joris, De Vylder, J., Saeys, Y., Goossens, B., & Philips, W. (2016). Decreasing time consumption of microscopy image segmentation through parallel processing on the GPU. In J Blanc Talon, C. Distante, W. Philips, D. Popescu, & P. Scheunders (Eds.), Lecture Notes in Computer Science (Vol. 10016, pp. 147–159). Presented at the 17th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS 2016), Cham, Switzerland: Springer.
Vancouver
1.
Roels J, De Vylder J, Saeys Y, Goossens B, Philips W. Decreasing time consumption of microscopy image segmentation through parallel processing on the GPU. In: Blanc Talon J, Distante C, Philips W, Popescu D, Scheunders P, editors. Lecture Notes in Computer Science. Cham, Switzerland: Springer; 2016. p. 147–59.
MLA
Roels, Joris, Jonas De Vylder, Yvan Saeys, et al. “Decreasing Time Consumption of Microscopy Image Segmentation Through Parallel Processing on the GPU.” Lecture Notes in Computer Science. Ed. J Blanc Talon et al. Vol. 10016. Cham, Switzerland: Springer, 2016. 147–159. Print.
@inproceedings{8505984,
  abstract     = {The computational performance of graphical processing units (GPUs) has improved significantly. Achieving speedup factors of more than 50x compared to single-threaded CPU execution are not uncommon due to parallel processing. This makes their use for high throughput microscopy image analysis very appealing. Unfortunately, GPU programming is not straightforward and requires a lot of programming skills and effort. Additionally, the attainable speedup factor is hard to predict, since it depends on the type of algorithm, input data and the way in which the algorithm is implemented. In this paper, we identify the characteristic algorithm and data-dependent properties that significantly relate to the achievable GPU speedup. We find that the overall GPU speedup depends on three major factors: (1) the coarse-grained parallelism of the algorithm, (2) the size of the data and (3) the computation/memory transfer ratio. This is illustrated on two types of well-known segmentation methods that are extensively used in microscopy image analysis: SLIC superpixels and high-level geometric active contours. In particular, we find that our used geometric active contour segmentation algorithm is very suitable for parallel processing, resulting in acceleration factors of 50x for 0.1 megapixel images and 100x for 10 megapixel images.},
  author       = {Roels, Joris and De Vylder, Jonas and Saeys, Yvan and Goossens, Bart and Philips, Wilfried},
  booktitle    = {Lecture Notes in Computer Science},
  editor       = {Blanc Talon, J and Distante, C and Philips, W and Popescu, D and Scheunders, P},
  isbn         = {9783319486796},
  issn         = {0302-9743},
  keyword      = {Microscopy,Image segmentation,GPGPU computing,FEATURES,MODELS},
  language     = {eng},
  location     = {Lecce, Italy},
  pages        = {147--159},
  publisher    = {Springer},
  title        = {Decreasing time consumption of microscopy image segmentation through parallel processing on the GPU},
  url          = {http://dx.doi.org/10.1007/978-3-319-48680-2\_14},
  volume       = {10016},
  year         = {2016},
}

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