Skip to main content

Advertisement

SpringerLink
Log in

Toward deep MRI segmentation for Alzheimer’s disease detection

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Alzheimer’s disease (AD) is an irreversible, progressive, and ultimately fatal brain degenerative disorder, no effective cures for it till now. Despite that, the available treatments can delay its progress. So, early detection of AD plays a crucial role in preventing and controlling its progress. Hippocampus (HC) is among the first impacted brain regions by AD. Its shape and volume are measured using a structural magnetic resonance image (MRI) to help AD diagnosis. Therefore, brain hippocampus segmentation is the building block for AD detection. This study’s main objective is to propose a deep learning Alzheimer’s disease hippocampus segmentation framework (DL-AHS) for automatic left and right hippocampus segmentation to detect and identify AD. The proposed DL-AHS framework is based on the U-Net architecture and estimated on the baseline coronal T1-weighted structural MRI data obtained from Alzheimer’s disease neuroimaging initiative (ADNI) and neuroimaging tools and resources collaboratory (NITRIC) datasets. The dataset is processed using the Medical Image Processing, Analysis, and Visualization (MIPAV) program. Besides, it is augmented using a deep convolutional generative adversarial network (DC-GAN). For left and right HC segmentation from other brain sub-regions, two architectures are proposed. The first utilizes simple hyperparameters tuning in the U-Net (SHPT-Net). The second employs a transfer learning technique in which the ResNet blocks are used in the U-Net (RESU-Net). The empirical results confirmed that the proposed framework achieves high performance, 94.34% accuracy, and 93.5% Dice similarity coefficient for SHPT-Net. Also, 97% accuracy and 94% Dice similarity coefficient are achieved for RESU-Net.

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Li H, Habes M, Wolk DA, Fan Y (2019) A deep learning model for early prediction of Alzheimer’s disease dementia based on hippocampal magnetic resonance imaging data. Alzheimer’s Dement 15(8):1059–1070

    Article  Google Scholar 

  2. Sevigny J et al (2016) The antibody aducanumab reduces Aβ plaques in Alzheimer’s disease. Nature 537(7618):50–56

    Article  Google Scholar 

  3. Wen J et al (2020) Convolutional neural networks for classification of Alzheimer’s disease: Overview and reproducible evaluation. Med Image Anal 63:101694

    Article  Google Scholar 

  4. Jain R, Aggarwal A, Kumar V (2021) Chapter 1—A review of deep learning-based disease detection in Alzheimer’s patients. In: Jude HD (ed) Handbook of decision support systems for neurological disorders. Academic Press, pp 1–19

    Google Scholar 

  5. Ding J, Kong W, Mou X, Wang S (2019) Construction of a transcriptional regulatory network of Alzheimer’s disease based on PANDA Algorithm. Interdiscip Sci Comput Life Sci 11(2):226–236

    Article  Google Scholar 

  6. Yang F et al (2020) Identification of key regulatory genes and pathways in the prefrontal cortex of Alzheimer’s disease. Interdiscip Sci Comput Life Sci 12(1):90–98

    Article  Google Scholar 

  7. Hosseini-Asl E, Gimel’farb G, El-Baz A (2016) Alzheimer’s disease diagnostics by a deeply supervised adaptable 3D convolutional network 502

  8. Nadal L et al (2020) Differential annualized rates of hippocampal subfields atrophy in aging and future Alzheimer’s clinical syndrome. Neurobiol Aging 90:75–83

    Article  Google Scholar 

  9. Carmo D, Silva B, Yasuda C, Rittner L, Lotufo R (2021) Hippocampus segmentation on epilepsy and Alzheimer’s disease studies with multiple convolutional neural networks. Heliyon 7(2):e06226

    Article  Google Scholar 

  10. Andersen P, Morris R, Amaral D, Bliss T, O’Keefe J (2006) The hippocampus book. Oxford University Press

    Book  Google Scholar 

  11. Petersen RC et al (2010) Alzheimer’s disease neuroimaging initiative (ADNI). Neurology 74(3):201–209

    Article  Google Scholar 

  12. Wang L et al (2003) Changes in hippocampal volume and shape across time distinguish dementia of the Alzheimer type from healthy aging. Neuroimage 20(2):667–682

    Article  Google Scholar 

  13. Duraisamy B, Shanmugam JV, Annamalai J (2019) Alzheimer disease detection from structural MR images using FCM based weighted probabilistic neural network. Brain Imaging Behav 13(1):87–110

    Article  Google Scholar 

  14. Dong H, Yang G, Liu F, Mo Y, Guo Y (2017) Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks. In: Annual conference on medical image understanding and analysis, pp 506–517

  15. Ghosh S, Das N, Das I, Maulik U (2019) Understanding deep learning techniques for image segmentation. ACM Comput Surv 52(4):1–58

    Article  Google Scholar 

  16. Shaken M et al (2016) Sub-cortical brain structure segmentation using F-CNN’S. Proc—Int Symp Biomed Imaging 2016:269–272

    Google Scholar 

  17. Seo H et al (2020) Machine learning techniques for biomedical image segmentation: an overview of technical aspects and introduction to state-of-art applications. Med Phys 47(5):e148–e167

    Article  Google Scholar 

  18. Singh SP, Wang L, Gupta S, Goli H, Padmanabhan P, Gulyás B et al (2020) 3D deep learning on medical images: a review. Sensors 20(18):5097

    Article  Google Scholar 

  19. Kalavathi P, Christy AAA, Priya T (2017) Detection of Alzheimer disease in MR brain images using FFCM method. Comput Methods, Commun Tech Inf 140–144

  20. Biju KS, Alfa SS, Lal K, Antony A, Akhil MK (2017) Alzheimer’s detection based on segmentation of MRI image. Procedia Comput Sci 115:474–481

    Article  Google Scholar 

  21. Yamanakkanavar N, Choi JY, Lee B (2020) MRI segmentation and classification of the human brain using deep learning for diagnosis of Alzheimer’s disease: a survey. Sensors (Switzerland) 20(11):1–31

    Article  Google Scholar 

  22. Shen D, Wu G, Suk H-I (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 19:221–248

    Article  Google Scholar 

  23. Litjens G et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88

    Article  Google Scholar 

  24. Li F, Tran L, Thung K-H, Ji S, Shen D, Li J (2015) A robust deep model for improved classification of AD/MCI patients. IEEE J Biomed Heal Inf 19(5):1610–1616

    Article  Google Scholar 

  25. Zeng N, Zhang H, Song B, Liu W, Li Y, Dobaie AM (2018) Facial expression recognition via learning deep sparse autoencoders. Neurocomputing 273:643–649

    Article  Google Scholar 

  26. Lin W et al (2018) Convolutional neural networks-based MRI image analysis for the Alzheimer’s disease prediction from mild cognitive impairment. Front Neurosci 12:777

    Article  Google Scholar 

  27. Chen Y (2019) Intelligent systems reference library 171 deep learning in healthcare

  28. Noh H, Hong S, Han B (2015) Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE international conference on computer vision, pp 1520–1528

  29. Lguensat R, Sun M, Fablet R, Tandeo P, Mason E, Chen G (2018) EddyNet: a deep neural network for pixel-wise classification of oceanic eddies. In: IGARSS 2018–2018 IEEE international geoscience and remote sensing symposium, pp 1764–1767

  30. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. Lect. Notes Comput. Sci. (includingSubser. Lect. Notes Artif. Intell. Lect. Notes Bioinformat) 9351:234–241

    Google Scholar 

  31. Dolz J, Desrosiers C, Ben Ayed I (2018) 3D fully convolutional networks for subcortical segmentation in MRI: a large-scale study. Neuroimage 170:456–470

    Article  Google Scholar 

  32. Allioui H, Sadgal M, Elfazziki A (2019) Deep MRI segmentation: A convolutional method applied to Alzheimer’s disease detection. Int J Adv Comput Sci Appl 10(11):365–371

    Google Scholar 

  33. Sun J, Yan S, Song C, Han B (2020) Dual-functional neural network for bilateral hippocampi segmentation and diagnosis of Alzheimer’s disease. Int J Comput Assist Radiol Surg 15(3):445–455

    Article  Google Scholar 

  34. Liu M et al (2020) A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer’s disease. Neuroimage 208:116459

    Article  Google Scholar 

  35. Chitradevi D, Prabha S, Prabhu AD (2020) Diagnosis of Alzheimer disease in MR brain images using optimization techniques. Neural Comput Appl 7:223–237

    Google Scholar 

  36. Nobakht S, Schaeffer M, Forkert ND, Nestor S, Black SE, Barber P (2021) Combined atlas and convolutional neural network-based segmentation of the hippocampus from MRI according to the ADNI harmonized protocol. Sensors 21(7):2427

    Article  Google Scholar 

  37. Chitradevi D, Prabha S (2020) Analysis of brain sub-regions using optimization techniques and deep learning method in Alzheimer disease. Appl Soft Comput J 86:105857

    Article  Google Scholar 

  38. Noor MBT, Zenia NZ, Kaiser MS, Al Mamun S, Mahmud M (2020) Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease, and schizophrenia. Brain Inf 7(1):1–21

    Article  Google Scholar 

  39. Jo T, Nho K, Saykin AJ (2019) Deep learning in Alzheimer’s disease: diagnostic classification and prognostic prediction using Neuroimaging data. Front Aging Neurosci 11:220

    Article  Google Scholar 

  40. Liu X, Deng Z, Yang Y (2019) Recent progress in semantic image segmentation. Artif Intell Rev 52(2):1089–1106

    Article  Google Scholar 

  41. Russakovsky O et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252

    Article  MathSciNet  Google Scholar 

  42. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D. Generative adversarial nets, pp 1–9

  43. Frid-Adar M, Diamant I, Klang E, Amitai M, Goldberger J, Greenspan H (2018) GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 321:321–331

    Article  Google Scholar 

  44. Venu SK, Ravula S (2021) Evaluation of deep convolutional generative adversarial networks for data augmentation of chest x-ray images. Futur Internet 13(1):1–13

    Google Scholar 

  45. Deepak S, Ameer PM (2019) Brain tumor classification using deep CNN features via transfer learning. Comput Biol Med 111:103345

    Article  Google Scholar 

  46. Eelbode T et al (2020) Optimization for medical image segmentation: theory and practice when evaluating with dice score or Jaccard index. IEEE Trans Med Imaging 39(11):3679–3690

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electrical Engineering Department, Faculty of Engineering, Damietta University, Damietta, Egypt

    Hadeer A. Helaly & Mahmoud Badawy

  2. Department of Computer Science and Informatics, Taibah University, Medina, Saudi Arabia

    Mahmoud Badawy

  3. The Head of Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt

    Amira Y. Haikal

Authors
  1. Hadeer A. Helaly
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mahmoud Badawy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amira Y. Haikal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hadeer A. Helaly.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Helaly, H.A., Badawy, M. & Haikal, A.Y. Toward deep MRI segmentation for Alzheimer’s disease detection. Neural Comput & Applic 34, 1047–1063 (2022). https://doi.org/10.1007/s00521-021-06430-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06430-8

Keywords

Search

Navigation