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HPC enables efficient 3D membrane segmentation in electron tomography

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Abstract

Electron Tomography (ET) is a powerful three-dimensional (3D) imaging technique used in structural biology and biomedicine to allow the visualization of the interior of cells at close-to-molecular resolution. Interpretation of the 3D volumes in ET is usually challenging due to the complexity of the cellular environment, noise conditions and other factors. Automated segmentation methods focused on membranes of the cells and organelles greatly facilitate visualization and interpretation of the 3D volumes. However, they are typically computationally expensive and spend significant processing time on standard computers. In this work, we introduce efficient implementations of one of the methods most commonly used in the ET field for membrane segmentation. They were developed by using High Performance Computing (HPC) techniques to make the most of modern CPU-based and GPU-based computing platforms. A thorough evaluation of the performance on state-of-the-art machines was carried out. The HPC implementations succeed in achieving remarkable speedups, which are around \(100\times\) on GPUs, and making it possible to process large 3D volumes in the order of seconds or a few minutes.

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References

  1. Turk M, Baumeister W (2020) The promise and the challenges of cryo-electron tomography. Federation Eur Biochem Soc (FEBS) Lett 594:3243–3261

    Article  Google Scholar 

  2. Herman GT (2009) Image reconstruction from projections: the fundamentals of computerized tomography, 2nd edn. Springer, London

    Book  MATH  Google Scholar 

  3. Fernandez JJ (2012) Computational methods for electron tomography. Micron 43:1010–1030

    Article  Google Scholar 

  4. Moreno JJ et al (2018) Tomoeed: Fast edge-enhancing denoising of tomographic volumes. Bioinformatics 34:3776–3778

    Article  Google Scholar 

  5. Martinez-Sanchez A et al (2020) Template-free detection and classification of membrane-bound complexes in cryo-electron tomograms. Nat Method 17:209–216

    Article  Google Scholar 

  6. Tasel SF et al (2016) A validated active contour method driven by parabolic arc model for detection and segmentation of mitochondria. J Struct Biol 194:253–271

    Article  Google Scholar 

  7. Luengo I et al (2017) SuRVoS: Super-region volume segmentation workbench. J Struct Biol 198:43–53

    Article  Google Scholar 

  8. Chen M et al (2017) Convolutional neural networks for automated annotation of cellular cryo-electron tomograms. Nat Methods 14:983–985

    Article  Google Scholar 

  9. Li R et al (2019) Automatic localization and identification of mitochondria in cellular electron cryo-tomography using faster-RCNN. BMC Bioinformatics 20(Suppl 3):132

    Article  Google Scholar 

  10. Fischer CA et al (2020) MitoSegNet: Easy-to-use deep learning segmentation for analyzing mitochondrial morphology. iScience 23(10):101601

    Article  Google Scholar 

  11. Moebel E et al (2021) Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms. Nat Methods 18:1386–1394

    Article  Google Scholar 

  12. Martinez-Sanchez A et al (2011) A differential structure approach to membrane segmentation in electron tomography. J Struct Biol 175:372–383

    Article  Google Scholar 

  13. Martinez-Sanchez A et al (2013) A ridge-based framework for segmentation of 3D electron microscopy datasets. J Struct Biol 181:61–70

    Article  Google Scholar 

  14. Martinez-Sanchez A et al (2014) Robust membrane detection based on tensor voting for electron tomography. J Struct Biol 186:49–61

    Article  Google Scholar 

  15. Page C, Hanein D, Volkmann N (2015) Accurate membrane tracing in three-dimensional reconstructions from electron cryotomography data. Ultramicroscopy 155:20–26

    Article  Google Scholar 

  16. Fernandez-Fernandez MR et al (2017) 3D electron tomography of brain tissue unveils distinct Golgi structures that sequester cytoplasmic contents in neurons. J Cell Sci 130:83–89

    Google Scholar 

  17. Chaikeeratisak V et al (2019) Viral capsid trafficking along treadmilling tubulin filaments in bacteria. Cell 177(7):1771–1780

    Article  Google Scholar 

  18. Bäuerlein FJB et al (2017) In situ architecture and cellular interactions of polyq inclusions. Cell 171(1):179–187

    Article  Google Scholar 

  19. Guo, Q.,. et al (2018) In situ structure of neuronal C9orf72 Poly-GA aggregates reveals proteasome recruitment. Cell 172(4):696–705

  20. Salfer M et al (2020) Reliable estimation of membrane curvature for cryo-electron tomography. PLoS Comput Biol 16:1007962

    Article  Google Scholar 

  21. Medioni G, Lee MS, Tang CK (2000) A Computational Framework for Segmentation and Grouping. Elsevier

  22. Briggs JA et al (2006) The mechanism of hiv-1 core assembly: insights from three-dimensional reconstructions of authentic virions. Structure 14:15–20

    Article  Google Scholar 

  23. Franken E et al (2006) An efficient method for tensor voting using steerable filters. Lect Notes Comput Sci 3954:228–240

    Article  Google Scholar 

  24. Freeman WT, Adelson EH (1991) The design and use of steerable filters. IEEE Trans Pattern Anal Mach Intell 13:891–906

    Article  Google Scholar 

  25. Frigo M, Johnson SG (2005) The design and implementation of fftw3. Proc IEEE 93:216–231

    Article  Google Scholar 

  26. Hennessy JL, Patterson DA (2019) Computer Architecture. A Quantitative Approach, 6th edn. Morgan Kauffman Publishers, Elsevier, Cambridge, MA, USA

    MATH  Google Scholar 

  27. Fernandez JJ (2008) High performance computing in structural determination by electron cryomicroscopy. J Struct Biol 164:1–6

    Article  Google Scholar 

  28. Tabik S et al (2007) High performance noise reduction for biomedical multidimensional data. Digit Signal Proc 17(4):724–736

    Article  Google Scholar 

  29. Fernandez JJ, Martinez JA (2010) Three-dimensional feature-preserving noise reduction for real-time electron tomography. Digital Signal Proc 20:1162–1172

    Article  Google Scholar 

  30. Agulleiro JI, Fernandez JJ (2011) Fast tomographic reconstruction on multicore computers. Bioinformatics 27(4):582–583

    Article  Google Scholar 

  31. Agulleiro JI, Fernández JJ (2012) Evaluation of multicore-optimized implementation for tomographic reconstruction. PLoS ONE 7:48261

    Article  Google Scholar 

  32. Butenhof DR (1997) Programming with POSIX Threads. Addison-Wesley Professional, Boston, MA, USA

    Google Scholar 

  33. Lawson CL et al (2016) EMDataBank unified data resource for 3DEM. Nucleic Acids Res 44:396–403

    Article  Google Scholar 

  34. Iudin A et al (2016) EMPIAR: A public archive for raw electron microscopy image data. Nat Method 13:387–388

    Article  Google Scholar 

  35. Bykov YS et al (2017) The structure of the copi coat determined within the cell. Elife 6:32493

    Article  Google Scholar 

  36. Yan D et al (2019) Sphingolipid biosynthesis modulates plasmodesmal ultrastructure and phloem unloading. Nature Plants 5:604–615

    Article  Google Scholar 

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Acknowledgements

This work has been supported by the projects and contracts: RTI2018-095993-B-I00, SAF2017-84565-R, PID2021-123278OB, PID2021-123424OB, TED2021-132020B (funded by MCIN/AEI/10.13039/501100011033/FEDER “A way to make Europe”); UAL18-TIC-A020020-B and P18-RT-1193 (both funded by Junta de Andalucía and FEDER); PAPI-21-GR-2011-0048 (funded by the University of Oviedo) and FUO-200-21 (funded by Thermo Fisher Scientific); and a FPU fellowship (FPU16/05946 funded by MCIN/AEI/10.13039/501100011033/ “El FSE invierte en tu futuro”) awarded to J.J. Moreno.

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Moreno, J.J., Garzón, E.M., Fernández, J.J. et al. HPC enables efficient 3D membrane segmentation in electron tomography. J Supercomput 78, 19097–19113 (2022). https://doi.org/10.1007/s11227-022-04607-z

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