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Efficient scheme to perform semantic segmentation on 3-D brain tumor using 3-D u-net architecture

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Abstract

Glioma is the most common type of brain tumor with varying level of malignancies and projection. Designing personalized therapy and to foresee response towards the therapy needs better understanding of tumor biology and diversification between tumors. Using different computational methods, accurate segmentation of tumors within the brain on MRI is the primary stride towards the understanding of tumor biology. The goal of current study is to draw an algorithm for MRI image segmentation of pre-treatment brain tumors and to evaluate its performance. In our research, we designed and implemented a novel 3D U-Net architecture for segmentation of sub-regions including edema, necrosis and enhancing tumor which are radiologically detectable. The group variance between tumor and non-tumorous spots is addressed by presenting weighted patch extraction scheme from tumor border regions. In its framework, context is captured using a contracting path and precise localization is performed by symmetric expanding path. In our study, the architecture based on Deep Convolutional Neural Network (DCNN) is trained on Brain Tumor Segmentation (BraTS) dataset of 750 patients among which 484 scans were labelled and 267 scans were used as training dataset. 3D patches were extracted from the dataset to train the system and results were assessed in terms of Specificity, Sensitivity and Dice Score. Our proposed system achieved Dice scores of 0.90 for whole tumor, 0.85 for tumor core, and 0.77 for enhancing tumor on dataset which shows potential of accurate intra-tumor segmentation of patch-based 3D U-Net architecture.

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Data availability

Dataset is freely available and can be downloaded from http://medicaldecathlon.com/.

References

  1. Ahmadvand P (2017) Machine learning driven active surfaces for 3D segmentation of tumour lesions in PET images

  2. Alom MZ et al (2019) Recurrent residual U-net for medical image segmentation. J Med Imaging (Bellingham) 6(1):014006

    PubMed  Google Scholar 

  3. Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495

    Article  PubMed  Google Scholar 

  4. Baid U et al (2020) A novel approach for fully automatic intra-tumor segmentation with 3D U-Net Architecture for Gliomas. 14(10)

  5. Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3D U-Net: learning dense volumetric segmentation from sparse annotation. Medical Image Computing and Computer-Assisted Intervention--MICCAI 2016: 19th International Conference, Athens, Greece, October 17–21, 2016, Proceedings, Part II 19. Springer, pp 424–432 

  6. Corso JJ et al (2008) Efficient multilevel brain tumor segmentation with integrated Bayesian model classification. IEEE Trans Med Imaging 27(5):629–640

    Article  CAS  PubMed  Google Scholar 

  7. Cui S et al (2018) Automatic semantic segmentation of brain gliomas from MRI images using a deep cascaded neural network. J Healthcare Eng 2018:4940593

    Article  Google Scholar 

  8. Gharavi SMH, Faghihimehr A (2021) Clinical Application of Artificial Intelligence in PET Cancer Imaging of Head and Neck. Artificial Intelligence and PET Imaging, Part 2, An Issue of PET Clinics, E-Book 17(1):65

  9. Hossain MZ et al (2019) A comprehensive survey of deep learning for image captioning. J ACM Comput Surv 51(6):Article 118

  10. Ibtehaz N, Rahman MS (2020) MultiResUNet : rethinking the U-net architecture for multimodal biomedical image segmentation. Neural Netw 121:74–87

    Article  PubMed  Google Scholar 

  11. Jäkel S, Dimou L (2017) Glial cells and their function in the adult brain: A Journey through the History of Their Ablation. 11(24)

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

    Article  PubMed  Google Scholar 

  13. Kofler F et al (2020) BraTS Toolkit: Translating BraTS brain tumor segmentation algorithms into clinical and scientific practice. 14(125)

  14. Li Z et al (2017) Low-grade glioma segmentation based on CNN with fully connected CRF. J Healthcare Eng 2017:9283480

    Article  Google Scholar 

  15. Li P et al (2018) Deep visual tracking: review and experimental comparison. Pattern Recogn 76:323–338

    Article  ADS  Google Scholar 

  16. Li Z, Kamnitsas K, Glocker B (2020) Analyzing overfitting under class imbalance in neural networks for image segmentation. IEEE Trans Med Imaging 40(3):1065–1077

    Article  Google Scholar 

  17. Li X, Li M, Yan P, Li G, Jiang Y, Luo H, Yin S (2023) Deep learning attention mechanism in medical image analysis: Basics and beyonds. Int J Netw Dyn Intell 2(1):93–116. https://doi.org/10.53941/ijndi0201006

  18. Li, S, et al. (2023) Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation. arXiv preprint arXiv:2302.03296

  19. Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, Scheithauer BW, Kleihues P (2007) The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol 114:97–109

  20. Milletari, F, Navab N, Ahmadi S (2016) V-Net: Fully convolutional neural networks for volumetric medical image segmentation. in 2016 Fourth International Conference on 3D Vision (3DV)

  21. Mo Y, Wu Y, Yang X, Liu F, Liao Y (2022) Review the state-of-the-art technologies of semantic segmentation based on deep learning. Neurocomputing 493:626–646

  22. Myronenko, A, Hatamizadeh A (2019) Robust semantic segmentation of brain tumor regions from 3d mris. in International MICCAI Brainlesion workshop. Springer

  23. Patel AP, Fisher JL, Nichols E, Abd-Allah F, Abdela J, Abdelalim A, Abraha HN, Agius D, Alahdab F, Alam T et al. (2016) Global, regional, and national burden of brain and other CNS cancer, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet Neurol 18(4):376–393

  24. Rawat W, Wang Z (2017) Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput 29(9):2352–2449

    Article  MathSciNet  PubMed  Google Scholar 

  25. Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18. Springer, pp 234–241

  26. Schwartzbaum JA, Fisher JL, Aldape KD, Wrensch M (2006) Epidemiology and molecular pathology of glioma. Nat Clin Pract Neurol 2(9):494–503

  27. Su R et al (2020) Multimodal glioma image segmentation using dual encoder structure and channel spatial attention block. Front Neurosci 14:586197–586197

    Article  PubMed  PubMed Central  Google Scholar 

  28. Van Meir EG et al (2010) Exciting new advances in neuro-oncology: the avenue to a cure for malignant glioma. CA Cancer J Clin 60(3):166–193

    Article  PubMed  PubMed Central  Google Scholar 

  29. Wang X et al (2022) Multimodal medical image segmentation using multi-scale context-aware network. Neurocomputing 486:135–146

    Article  Google Scholar 

  30. Yeung M, Sala E, Schönlieb C-B, Rundo L (2022) Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Comput Med Imaging Graph 95:102026

  31. Zhao ZQ et al (2019) Object detection with deep learning: a review. IEEE Trans Neural Netw Learn Syst 30(11):3212–3232

    Article  PubMed  Google Scholar 

  32. Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2018) UNet++: A nested U-net architecture for medical image segmentation. Deep learning in medical image analysis and multimodal learning for clinical decision support : 4th international workshop, DLMIA 2018, and 8th international workshop, ML-CDS 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4. Springer, pp 3–11

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Author notes

  1. Zeeshan Shaukat and Qurratul Ain Farooq equally Contributed.

Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, China

    Zeeshan Shaukat, Chuangbai Xiao & Saqib Ali

  2. Faculty of Environmental & Life Sciences, Beijing University of Technology, Beijing, China

    Qurratul Ain Farooq

  3. Department of Computer Science, Sukkur IBA University, Sukkur, 65200, Pakistan

    Faheem Akhtar

  4. Faculty of Computing & IT, University of Sialkot, Sialkot, 51300, Pakistan

    Muhammad Azeem

  5. Department of Management Science and Engineering, Beihang University, Beijing, China

    Abdul Ahad Zulfiqar

Authors
  1. Zeeshan Shaukat
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  2. Qurratul Ain Farooq
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  3. Chuangbai Xiao
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  4. Saqib Ali
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  5. Faheem Akhtar
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  6. Muhammad Azeem
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Correspondence to Zeeshan Shaukat or Chuangbai Xiao.

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Shaukat, Z., Farooq, Q.A., Xiao, C. et al. Efficient scheme to perform semantic segmentation on 3-D brain tumor using 3-D u-net architecture. Multimed Tools Appl 83, 25121–25134 (2024). https://doi.org/10.1007/s11042-023-16458-8

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  • DOI: https://doi.org/10.1007/s11042-023-16458-8

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