Skip to main content
SpringerLink
Log in

Optimization of Deep Learning Based Brain Extraction in MRI for Low Resource Environments

  • Conference paper
  • First Online:
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 12962))

Included in the following conference series:

Abstract

Brain extraction is an indispensable step in neuro-imaging with a direct impact on downstream analyses. Most such methods have been developed for non-pathologically affected brains, and hence tend to suffer in performance when applied on brains with pathologies, e.g., gliomas, multiple sclerosis, traumatic brain injuries. Deep Learning (DL) methodologies for healthcare have shown promising results, but their clinical translation has been limited, primarily due to these methods suffering from i) high computational cost, and ii) specific hardware requirements, e.g., DL acceleration cards. In this study, we explore the potential of mathematical optimizations, towards making DL methods amenable to application in low resource environments. We focus on both the qualitative and quantitative evaluation of such optimizations on an existing DL brain extraction method, designed for pathologically-affected brains and agnostic to the input modality. We conduct direct optimizations and quantization of the trained model (i.e., prior to inference on new data). Our results yield substantial gains, in terms of speedup, latency, throughput, and reduction in memory usage, while the segmentation performance of the initial and the optimized models remains stable, i.e., as quantified by both the Dice Similarity Coefficient and the Hausdorff Distance. These findings support post-training optimizations as a promising approach for enabling the execution of advanced DL methodologies on plain commercial-grade CPUs, and hence contributing to their translation in limited- and low- resource clinical environments.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
eBook
USD 109.00
Price excludes VAT (USA)
Softcover Book
USD 139.99
Price excludes VAT (USA)

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://github.com/CBICA/BrainMaGe.

  2. 2.

    github.com/pyushkevich/greedy, hash: 1a871c1, Last accessed: 27/May/2020.

  3. 3.

    itksnap.org, version: 3.8.0, last accessed: 27/May/2020.

  4. 4.

    www.cbica.upenn.edu/captk, version: 1.8.1, last accessed: 11/February/2021.

  5. 5.

    https://github.com/CBICA/BrainMaGe.

  6. 6.

    https://github.com/CBICA/GaNDLF.

References

  1. Smith, S.M.: “Bet: Brain extraction tool,” FMRIB TR00SMS2b, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain). Department of Clinical Neurology, Oxford University, John Radcliffe Hospital, Headington, UK (2000)

    Google Scholar 

  2. Smith, S.M.: Fast robust automated brain extraction. Hum. Brain Mapp. 17(3), 143–155 (2002)

    Article  Google Scholar 

  3. Schwarz, C.G., et al.: Identification of anonymous MRI research participants with face-recognition software. N. Engl. J. Med. 381(17), 1684–1686 (2019)

    Article  Google Scholar 

  4. Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629 (2018)

  5. Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017)

    Article  Google Scholar 

  6. Bakas, S., et la.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. Cancer Imaging Arch. (2017). https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q

  7. Gitler, A.D., Dhillon, P., Shorter, J.: Neurodegenerative disease: models, mechanisms, and a new hope. Disease Models Mech. 10, 499–502 (2017). 28468935[pmid]

    Google Scholar 

  8. Ostrom, Q.T., Rubin, J.B., Lathia, J.D., Berens, M.E., Barnholtz-Sloan, J.S.: Females have the survival advantage in glioblastoma. Neuro-oncol. 20, 576–577 (2018). 29474647[pmid]

    Google Scholar 

  9. Herrlinger, U., et al.: Lomustine-temozolomide combination therapy versus standard temozolomide therapy in patients with newly diagnosed glioblastoma with methylated MGMT promoter (CeTeG/NOA-09): a randomised, open-label, phase 3 trial. Lancet 393, 678–688 (2019)

    Google Scholar 

  10. Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49

    Chapter  Google Scholar 

  11. Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: NNU-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)

    Article  Google Scholar 

  12. Isensee, F., et al.: Automated brain extraction of multisequence MRI using artificial neural networks. Hum. Brain Mapp. 40(17), 4952–4964 (2019)

    Article  Google Scholar 

  13. Pati, S., et al.: Gandlf: a generally nuanced deep learning framework for scalable end-to-end clinical workflows in medical imaging. arXiv preprint arXiv:2103.01006 (2021)

  14. Thakur, S.P.: Skull-stripping of glioblastoma MRI scans using 3D deep learning. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019. LNCS, vol. 11992, pp. 57–68. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46640-4_6

    Chapter  Google Scholar 

  15. Thakur, S., et al.: Brain extraction on MRI scans in presence of diffuse glioma: multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training. Neuroimage 220, 117081 (2020)

    Article  Google Scholar 

  16. Bhalerao, M., Thakur, S.: Brain tumor segmentation based on 3D residual U-net. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019. LNCS, vol. 11993, pp. 218–225. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46643-5_21

    Chapter  Google Scholar 

  17. Kamnitsas, K., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)

    Article  Google Scholar 

  18. Leung, K.K., et al.: Brain maps: an automated, accurate and robust brain extraction technique using a template library. Neuroimage 55(3), 1091–1108 (2011)

    Article  Google Scholar 

  19. Paszke, A., et al..: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, pp. 8026–8037 (2019)

    Google Scholar 

  20. Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), vol. 16, pp. 265–283 (2016)

    Google Scholar 

  21. Lin, H.W., Tegmark, M., Rolnick, D.: Why does deep and cheap learning work so well? J. Stat. Phys. 168, 1223–1247 (2017)

    Article  MathSciNet  Google Scholar 

  22. Jouppi, N.P., et al.: In-datacenter performance analysis of a tensor processing unit (2017)

    Google Scholar 

  23. Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., Bengio, Y.: Quantized neural networks: training neural networks with low precision weights and activations (2016)

    Google Scholar 

  24. Clark, K., et al.: The cancer imaging archive (TCIA): Maintaining and operating a public information repository. J. Digit. Imaging 26, 1045–1057 (2013)

    Article  Google Scholar 

  25. Scarpace, L., et al.: Radiology data from the cancer genome atlas glioblastoma multiforme [TCGA-GBM] collection. Cancer Imaging Arch. 11(4), 1 (2016)

    Google Scholar 

  26. Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2014)

    Article  Google Scholar 

  27. Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Arch. (2017). https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF

  28. Baid, U., et al.: The RSNA-ASNR-MICCAI brats 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv preprint arXiv:2107.02314 (2021)

  29. Cox, R., et al.: A (sort of) new image data format standard: Nifti-1: We 150. Neuroimage 22 (2004)

    Google Scholar 

  30. Rohlfing, T., Zahr, N.M., Sullivan, E.V., Pfefferbaum, A.: The sri24 multichannel atlas of normal adult human brain structure. Hum. Brain Mapp. 31, 798–819 (2009)

    Article  Google Scholar 

  31. Yushkevich, P.A., Pluta, J., Wang, H., Wisse, L.E., Das, S., Wolk, D.: Fast automatic segmentation of hippocampal subfields and medial temporal lobe subregions in 3 tesla and 7 tesla t2-weighted MRI. Alzheimer’s & Dementia: J. Alzheimer’s Assoc. 12(7), P126–P127 (2016)

    Google Scholar 

  32. Joshi, S., Davis, B., Jomier, M., Gerig, G.: Unbiased diffeomorphic atlas construction for computational anatomy. Neuroimage 23, S151–S160 (2004)

    Article  Google Scholar 

  33. Yushkevich, P.A., et al.: User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3), 1116–1128 (2006)

    Article  Google Scholar 

  34. Yushkevich, P.A., et al.: User-guided segmentation of multi-modality medical imaging datasets with ITK-snap. Neuroinformatics 17(1), 83–102 (2019)

    Article  Google Scholar 

  35. Davatzikos, C., et al.: Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome. J. Med. Imaging 5(1), 011018 (2018)

    Article  Google Scholar 

  36. Rathore, S., et al.: Brain cancer imaging phenomics toolkit (brain-CaPTk): an interactive platform for quantitative analysis of glioblastoma. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 133–145. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75238-9_12

    Chapter  Google Scholar 

  37. Pati, S., et al.: The cancer imaging phenomics toolkit (CaPTk): technical overview. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019. LNCS, vol. 11993, pp. 380–394. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46643-5_38

    Chapter  Google Scholar 

  38. A. Fathi Kazerooni, H. Akbari, G. Shukla, C. Badve, J. D. Rudie, C. Sako, S. Rathore, S. Bakas, S. Pati, A. Singh, et al., "Cancer imaging phenomics via captk: multi-institutional prediction of progression-free survival and pattern of recurrence in glioblastoma," JCO clinical cancer informatics, vol. 4, pp. 234–244, 2020

    Google Scholar 

  39. Rathore, S., et al.: Multi-institutional noninvasive in vivo characterization of IDH, 1p/19q, and egfrviii in glioma using neuro-cancer imaging phenomics toolkit (neuro-captk). Neuro-oncol. Adv. 2(Supplement_4), iv22–iv34 (2020)

    Google Scholar 

  40. Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imaging 17(1), 87–97 (1998)

    Article  Google Scholar 

  41. Tustison, N.J., et al.: N4itk: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)

    Article  Google Scholar 

  42. Larsen, C.T., Iglesias, J.E., Van Leemput, K.: N3 bias field correction explained as a Bayesian modeling method. In: Cardoso, M.J., Simpson, I., Arbel, T., Precup, D., Ribbens, A. (eds.) BAMBI 2014. LNCS, vol. 8677, pp. 1–12. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12289-2_1

    Chapter  Google Scholar 

  43. Iqbal, H.: Harisiqbal88/plotneuralnet v1.0.0, December 2018

    Google Scholar 

  44. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  45. Drozdzal, M., Vorontsov, E., Chartrand, G., Kadoury, S., Pal, C.: The importance of skip connections in biomedical image segmentation. In: Carneiro, G., et al. (eds.) LABELS/DLMIA -2016. LNCS, vol. 10008, pp. 179–187. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46976-8_19

    Chapter  Google Scholar 

  46. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  47. Openvino™ toolkit overview (2021). https://docs.openvinotoolkit.org/latest/index.html

  48. Pytorch extension for openvino™ model optimizer (2021). https://github.com/openvinotoolkit/openvino_contrib/tree/master/modules/mo_pytorch

  49. Cyphers, S., et al.: Intel nGraph: an intermediate representation, compiler, and executor for deep learning. arXiv preprint arXiv:1801.08058 (2018)

  50. Wu, H., Judd, P., Zhang, X., Isaev, M., Micikevicius, P.: Integer quantization for deep learning inference: principles and empirical evaluation. CoRR, vol. abs/2004.09602 (2020)

    Google Scholar 

  51. Choukroun, Y., Kravchik, E., Yang, F., Kisilev, P.: Low-bit quantization of neural networks for efficient inference. In: ICCV Workshops, pp. 3009–3018 (2019)

    Google Scholar 

  52. Quantization algorithms. https://intellabs.github.io/distiller/algo_quantization.html

  53. Int8 inference (2021). https://oneapi-src.github.io/oneDNN/dev_guide_inference_int8.html

  54. Tailor, S.A., Fernandez-Marques, J., Lane, N.D.: Degree-quant: quantization-aware training for graph neural networks. arXiv preprint arXiv:2008.05000 (2020)

  55. Fang, J., Shafiee, A., Abdel-Aziz, H., Thorsley, D., Georgiadis, G., Hassoun, J.H.: Post-training piecewise linear quantization for deep neural networks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12347, pp. 69–86. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58536-5_5

    Chapter  Google Scholar 

  56. Openvino™ toolkit accuracyaware method (2021). https://docs.openvinotoolkit.org/latest/workbench_docs_Workbench_DG_Int_8_Quantization.html

  57. Zijdenbos, A.P., Dawant, B.M., Margolin, R.A., Palmer, A.C.: Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Trans. Med. Imaging 13(4), 716–724 (1994)

    Article  Google Scholar 

  58. Reinke, A., et al.: Common limitations of performance metrics in biomedical image analysis. Med. Imaging Deep Learn. (2021)

    Google Scholar 

  59. Arafa, M., et al.: Cascade lake: next generation intel Xeon scalable processor. IEEE Micro 39(2), 29–36 (2019)

    Article  Google Scholar 

  60. Lower numerical precision deep learning inference and training (2018). https://software.intel.com/content/www/us/en/develop/articles/lower-numerical-precision-deep-learning-inference-and-training.html

  61. Davatzikos, C., et al.: Ai-based prognostic imaging biomarkers for precision neuro-oncology: the respond consortium. Neuro-oncol. 22(6), 886–888 (2020)

    Google Scholar 

  62. Bakas, S., et al.: iGlass: imaging integration into the glioma longitudinal analysis consortium. Neuro Oncol. 22(10), 1545–1546 (2020)

    Article  Google Scholar 

Download references

Acknowledgments

Research reported in this publication was partly supported by the National Cancer Institute (NCI) and the National Institute of Neurological Disorders and Stroke (NINDS) of the National Institutes of Health (NIH), under award numbers NCI:U01CA242871 and NINDS:R01NS042645. The content of this publication is solely the responsibility of the authors and does not represent the official views of the NIH.

Author information

Authors and Affiliations

  1. Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA

    Siddhesh P. Thakur, Sarthak Pati, Chiharu Sako & Spyridon Bakas

  2. Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Siddhesh P. Thakur, Sarthak Pati, Chiharu Sako & Spyridon Bakas

  3. Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Siddhesh P. Thakur, Sarthak Pati & Spyridon Bakas

  4. Department of Informatics, Technical University of Munich, Munich, Germany

    Sarthak Pati

  5. Intel Health and Life Sciences, Intel Corporation, Santa Clara, CA, USA

    Ravi Panchumarthy, Deepthi Karkada, Junwen Wu, Dmitry Kurtaev & Prashant Shah

Authors
  1. Siddhesh P. Thakur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sarthak Pati
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ravi Panchumarthy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Deepthi Karkada
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Junwen Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dmitry Kurtaev
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chiharu Sako
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Prashant Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Spyridon Bakas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Spyridon Bakas .

Editor information

Editors and Affiliations

  1. Sano Centre for Computational Personaliz, Kraków, Poland

    Alessandro Crimi

  2. University of Pennsylvania, Philadelphia, PA, USA

    Spyridon Bakas

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Thakur, S.P. et al. (2022). Optimization of Deep Learning Based Brain Extraction in MRI for Low Resource Environments. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12962. Springer, Cham. https://doi.org/10.1007/978-3-031-08999-2_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-08999-2_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08998-5

  • Online ISBN: 978-3-031-08999-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation