Decreasing Time Consumption of Microscopy Image Segmentation Through Parallel Processing on the GPU

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.

[1]  Mila Nikolova,et al.  Algorithms for Finding Global Minimizers of Image Segmentation and Denoising Models , 2006, SIAM J. Appl. Math..

[2]  Andrew Lumsdaine,et al.  GPU Programming in Rust: Implementing High-Level Abstractions in a Systems-Level Language , 2013, 2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum.

[3]  Ian Reid,et al.  gSLIC: a real-time implementation of SLIC superpixel segmentation , 2011 .

[4]  Leila Meziou,et al.  Confocal microscopy segmentation using active contour based on alpha(α)-divergence , 2011, 2011 18th IEEE International Conference on Image Processing.

[5]  Srinivas C. Turaga,et al.  Connectomic reconstruction of the inner plexiform layer in the mouse retina , 2013, Nature.

[6]  Danny Crookes,et al.  GPU implementation of map-MRF for microscopy imagery segmentation , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[7]  Pascal Fua,et al.  Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks With Learned Shape Features , 2012, IEEE Transactions on Medical Imaging.

[8]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Falko Kuester,et al.  GPU-Based Active Contour Segmentation Using Gradient Vector Flow , 2006, ISVC.

[10]  Gongping Yang,et al.  Hierarchical level features based trainable segmentation for electron microscopy images , 2013, BioMedical Engineering OnLine.

[11]  Zhiwen Liu,et al.  Segmentation and Tracking of Lymphocytes Based on Modified Active Contour Models in Phase Contrast Microscopy Images , 2015, Comput. Math. Methods Medicine.

[12]  Wilfried Philips,et al.  Quasar — A new heterogeneous programming framework for image and video processing algorithms on CPU and GPU , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[13]  Antonin Chambolle,et al.  Dual Norms and Image Decomposition Models , 2005, International Journal of Computer Vision.

[14]  H. Sebastian Seung,et al.  Learning to Agglomerate Superpixel Hierarchies , 2011, NIPS.

[15]  Frédo Durand,et al.  Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines , 2013, PLDI 2013.

[16]  Philipp J. Keller,et al.  Real-Time Three-Dimensional Cell Segmentation in Large-Scale Microscopy Data of Developing Embryos. , 2016, Developmental cell.

[17]  Xavier Bresson,et al.  Fast Global Minimization of the Active Contour/Snake Model , 2007, Journal of Mathematical Imaging and Vision.

[18]  Pascal Fua,et al.  Refining Mitochondria Segmentation in Electron Microscopy Imagery with Active Surfaces , 2014, ECCV Workshops.