Skip to main content
SpringerLink
Log in

Spherical coordinates transformation pre-processing in Deep Convolution Neural Networks for brain tumor segmentation in MRI

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Magnetic Resonance Imaging (MRI) is used in everyday clinical practice to assess brain tumors. Deep Convolutional Neural Networks (DCNN) have recently shown very promising results in brain tumor segmentation tasks; however, DCNN models fail the task when applied to volumes that are different from the training dataset. One of the reasons is due to the lack of data standardization to adjust for different models and MR machines. In this work, a 3D spherical coordinates transform during the pre-processing phase has been hypothesized to improve DCNN models’ accuracy and to allow more generalizable results even when the model is trained on small and heterogeneous datasets and translated into different domains. Indeed, the spherical coordinate system avoids several standardization issues since it works independently of resolution and imaging settings. The model trained on spherical transform pre-processed inputs resulted in superior performance over the Cartesian-input trained model on predicting gliomas’ segmentation on Tumor Core and Enhancing Tumor classes, achieving a further improvement in accuracy by merging the two models together. The proposed model is not resolution-dependent, thus improving segmentation accuracy and theoretically solving some transfer learning problems related to the domain shifting, at least in terms of image resolution in the datasets.

Graphical abstract

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Thrall JH, Li X, Li Q et al (2018) Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. J Am Coll Radiol 15:504–508. https://doi.org/10.1016/j.jacr.2017.12.026

    Article  PubMed  Google Scholar 

  2. Menze BH, Jakab A, Bauer S et al (2015) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34:1993–2024. https://doi.org/10.1109/TMI.2014.2377694

    Article  PubMed  Google Scholar 

  3. Bakas S, Reyes M, Jakab A, et al. (2018) Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

  4. Challen R, Denny J, Pitt M et al (2019) Artificial intelligence, bias and clinical safety. BMJ Qual Saf 28:231–237. https://doi.org/10.1136/bmjqs-2018-008370

    Article  PubMed  PubMed Central  Google Scholar 

  5. Allen B, Seltzer SE, Langlotz CP et al (2019) A road map for translational research on artificial intelligence in medical imaging: from the 2018 National Institutes of Health/RSNA/ACR/The Academy Workshop. J Am Coll Radiol 16:1179–1189. https://doi.org/10.1016/j.jacr.2019.04.014

    Article  PubMed  Google Scholar 

  6. Kundel HL, Nodine CF (1983) A visual concept shapes image perception. Radiology 146:363–368. https://doi.org/10.1148/radiology.146.2.6849084

    Article  CAS  PubMed  Google Scholar 

  7. Nodine CF, Krupinski EA (1998) Perceptual skill, radiology expertise, and visual test performance with NINA and WALDO. Acad Radiol 5:603–612. https://doi.org/10.1016/S1076-6332(98)80295-X

    Article  CAS  PubMed  Google Scholar 

  8. Isensee F, Kickingereder P, Wick W, et al. (2019) No New-Net. pp 234–244

  9. Manning D, Ethell S, Donovan T, Crawford T (2006) How do radiologists do it? The influence of experience and training on searching for chest nodules. Radiography 12:134–142. https://doi.org/10.1016/j.radi.2005.02.003

    Article  Google Scholar 

  10. Bertram R, Helle L, Kaakinen JK, Svedström E (2013) The effect of expertise on eye movement behaviour in medical image perception. PLoS ONE 8:e66169. https://doi.org/10.1371/journal.pone.0066169

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Donovan T, Manning DJ (2007) The radiology task: Bayesian theory and perception. Br J Radiol 80:389–391. https://doi.org/10.1259/bjr/98148548

    Article  CAS  PubMed  Google Scholar 

  12. Kundel HL, Nodine CF, Conant EF, Weinstein SP (2007) Holistic component of image perception in mammogram interpretation: gaze-tracking study. Radiology 242:396–402. https://doi.org/10.1148/radiol.2422051997

    Article  PubMed  Google Scholar 

  13. Kundel HL, La Follette PS (1972) Visual search patterns and experience with radiological images. Radiology 103:523–528. https://doi.org/10.1148/103.3.523

    Article  CAS  PubMed  Google Scholar 

  14. Kundel HL, John Wright D (1969) The influence of prior knowledge on visual search strategies during the viewing of chest radioqraphs. Radiology 93:315–320. https://doi.org/10.1148/93.2.315

    Article  CAS  PubMed  Google Scholar 

  15. Thorpe S, Fize D, Marlot C (1996) Speed of processing in the human visual system. Nature 381:520–522. https://doi.org/10.1038/381520a0

    Article  CAS  PubMed  Google Scholar 

  16. Antonopoulos M, Dionysiou D, Stamatakos G, Uzunoglu N (2019) Three-dimensional tumor growth in time-varying chemical fields: a modeling framework and theoretical study. BMC Bioinformatics 20:442. https://doi.org/10.1186/s12859-019-2997-9

    Article  PubMed  PubMed Central  Google Scholar 

  17. Torres Hoyos F, Navarro RB, Vergara Villadiego J, Guerrero-Martelo M (2018) Geometrical study of astrocytomas through fractals and scaling analysis. Appl Radiat Isot 141:250–256. https://doi.org/10.1016/j.apradiso.2018.05.020

    Article  CAS  PubMed  Google Scholar 

  18. Murray JM, Kaissis G, Braren R, Kleesiek J (2020) A primer on radiomics. Radiologe 60:32–41. https://doi.org/10.1007/s00117-019-00617-w

    Article  PubMed  Google Scholar 

  19. Fan Y, Feng M, Wang R (2019) Application of radiomics in central nervous system diseases: a systematic literature review. Clin Neurol Neurosurg 187:105565. https://doi.org/10.1016/j.clineuro.2019.105565

    Article  PubMed  Google Scholar 

  20. Jang K, Russo C, Di Ieva A (2020) Radiomics in gliomas: clinical implications of computational modeling and fractal-based analysis. Neuroradiology 62:771–790. https://doi.org/10.1007/s00234-020-02403-1

    Article  PubMed  Google Scholar 

  21. Di Ieva A (2016) The fractal geometry of the brain. Springer New York, New York

    Book  Google Scholar 

  22. Di Ieva A, Grizzi F, Ceva-Grimaldi G et al (2007) Fractal dimension as a quantitator of the microvasculature of normal and adenomatous pituitary tissue. J Anat 211:673–680. https://doi.org/10.1111/j.1469-7580.2007.00804.x

    Article  PubMed  PubMed Central  Google Scholar 

  23. Di Ieva A, Le Reste P-J, Carsin-Nicol B et al (2016) Diagnostic value of fractal analysis for the differentiation of brain tumors using 3-Tesla magnetic resonance susceptibility-weighted imaging. Neurosurgery 79:839–846. https://doi.org/10.1227/NEU.0000000000001308

    Article  PubMed  Google Scholar 

  24. Milosevic NT, Di Ieva A, Jelinek H, Rajkovic N (2017) Box-counting method in quantitative analysis of images of the brain. In: 2017 21st International Conference on Control Systems and Computer Science (CSCS). pp 343–349

  25. Di Ieva A, Göd S, Grabner G et al (2013) Three-dimensional susceptibility-weighted imaging at 7 T using fractal-based quantitative analysis to grade gliomas. Neuroradiology 55:35–40. https://doi.org/10.1007/s00234-012-1081-1

    Article  PubMed  Google Scholar 

  26. Di Ieva A, Esteban FJ, Grizzi F et al (2015) Fractals in the neurosciences, Part II. Neurosci 21:30–43. https://doi.org/10.1177/1073858413513928

    Article  Google Scholar 

  27. Grizzi F, Castello A, Qehajaj D et al (2019) The complexity and fractal geometry of nuclear medicine images. Mol Imaging Biol 21:401–409. https://doi.org/10.1007/s11307-018-1236-5

    Article  CAS  PubMed  Google Scholar 

  28. Petrujkić K, Milošević N, Rajković N et al (2019) Computational quantitative MR image features - a potential useful tool in differentiating glioblastoma from solitary brain metastasis. Eur J Radiol 119:108634. https://doi.org/10.1016/j.ejrad.2019.08.003

    Article  PubMed  Google Scholar 

  29. Worrall DE, Garbin SJ, Turmukhambetov D, Brostow GJ (2017) Harmonic networks: deep translation and rotation equivariance. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp 7168–7177

  30. Depeursinge A, Fageot J, Andrearczyk V, et al. (2018) Rotation invariance and directional sensitivity: spherical harmonics versus radiomics features. pp 107–115

  31. Cohen TS, Welling M (2016) Group equivariant convolutional networks. Proc Int Conf Mach Learn

  32. Winkels M, Cohen TS (2019) Pulmonary nodule detection in CT scans with equivariant CNNs. Med Image Anal 55:15–26. https://doi.org/10.1016/j.media.2019.03.010

    Article  PubMed  Google Scholar 

  33. Andrearczyk V, Oreiller V, Fageot J, et al. (2019) Solid spherical energy (SSE) CNNs for efficient 3D medical image analysis. Irish Mach Vis Image Process Conf

  34. Andrearczyk V, Fageot J, Oreiller V, et al. (2019) Exploring local rotation invariance in 3D CNNs with steerable filters. Proc Mach Learn Res

  35. Xu Y, Xiao T, Zhang J, et al. (2014) Scale-invariant convolutional neural networks. ArXiv

  36. Edelman JA, Mieses AM, Konnova K, Shiu D (2017) The effect of object-centered instructions in Cartesian and polar coordinates on saccade vector. J Vis 17:2. https://doi.org/10.1167/17.3.2

    Article  PubMed  PubMed Central  Google Scholar 

  37. LIN C-S, CHEN H-T, LIN C-H, et al (2005) Polar coordinate mapping method for an improved infrared eye-tracking system. Biomed Eng Appl Basis Commun 17:141–146. https://doi.org/10.4015/S1016237205000226

    Article  Google Scholar 

  38. Cantoni V (1994) Human and machine vision : analogies and divergencies. Springer

    Book  Google Scholar 

  39. Esteves C, Allen-Blanchette C, Zhou X, Daniilidis K (2018) Polar transformer networks. ArXiv

  40. Eric W. Weisstein (2005) Spherical coordinates -- from MathWorld-a Wolfram web resource

  41. Myronenko A (2019) 3D MRI brain tumor segmentation using autoencoder regularization. In: Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes 2018. Lecture Notes in Computer Science, Springer. Cham, pp 311–320

  42. Kingma DP, Welling M (2014) Auto-encoding variational Bayes. ICLR 2014 - Conf Track Proc

  43. Doersch C (2016) Tutorial on variational autoencoders. ArXiv

  44. Liu L, Jiang H, He P, et al. (2019) On the variance of the adaptive learning rate and beyond. ICLR 2019

  45. Simpson AL, Antonelli M, Bakas S, et al. (2019) A large annotated medical image dataset for the development and evaluation of segmentation algorithms. ArXiv

  46. Nalepa J, Cwiek M, Dudzik W, et al. (2019) Data augmentation via image registration. In: 2019 IEEE International Conference on Image Processing (ICIP). IEEE, pp 4250–4254

  47. Nalepa J, Marcinkiewicz M, Kawulok M (2019) Data augmentation for brain-tumor segmentation: a review. Front Comput Neurosci 13:. https://doi.org/10.3389/fncom.2019.00083

  48. Ribalta Lorenzo P, Nalepa J, Bobek-Billewicz B et al (2019) Segmenting brain tumors from FLAIR MRI using fully convolutional neural networks. Comput Methods Programs Biomed 176:135–148. https://doi.org/10.1016/j.cmpb.2019.05.006

    Article  PubMed  Google Scholar 

  49. Xu Y, Goodacre R (2018) On splitting training and validation set: a comparative study of cross-validation, bootstrap and systematic sampling for estimating the generalization performance of supervised learning. J Anal Test 2:249–262. https://doi.org/10.1007/s41664-018-0068-2

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Health and Human Science, Macquarie University, 1st floor, 75 Talavera Rd, Macquarie Park, Sydney, NSW, 2109, Australia

    Carlo Russo, Sidong Liu & Antonio Di Ieva

  2. Australian Institute of Health Innovation, Centre for Health Informatics, Macquarie University, Sydney, Australia

    Sidong Liu

Authors
  1. Carlo Russo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sidong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Antonio Di Ieva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Carlo Russo.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Russo, C., Liu, S. & Di Ieva, A. Spherical coordinates transformation pre-processing in Deep Convolution Neural Networks for brain tumor segmentation in MRI. Med Biol Eng Comput 60, 121–134 (2022). https://doi.org/10.1007/s11517-021-02464-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-021-02464-1

Keywords

Search

Navigation