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An Exhaustive Analytical Study of U-Net Architecture on Two Diverse Biomedical Imaging Datasets of Electron Microscopy Drosophila ssTEM and Brain MRI BraTS-2021 for Segmentation

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Abstract

Biomedical image segmentation is a mechanism that distinguishes the boundaries of various lesion regions within the image, be it 2D or 3D. In the current scenario, various soft and hard computing approaches have been used for medical image segmentation purposes. This article analyzes deep learning-based U-Net architecture for efficient segmentation by possible variations of its different important hyper-parameters that affect the U-Net performance. This article also investigates U-Net architecture’s performance to generate the segmented output over two diverse biomedical imaging datasets, such as brain MRI scans and serial section Transmission Electron Microscopy (ssTEM) dataset of the drosophila first instar larva VNC, hence making 48 possible combinations of execution of the U-Net model. The performance of the segmentation process has been evaluated using metrics like dice similarity coefficient and accuracy, while cross-entropy is considered the loss function. In doing so, an insight into the overall performance of U-Net on biomedical image segmentation problems has been obtained. The best results for accuracy and dice coefficient values out of all possible combinations made in this study come as 0.9881 and 0.9934, respectively. The main motive of this work is to provide an exhaustive and efficient analysis of the U-Net architecture for biomedical image segmentation and subsequent analysis.

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References

  1. Jena B, Nayak GK, Saxena S (2022) Convolutional neural network and its pretrained models for image classification and object detection: A survey. Concurrency and Computation: Concurr Comput Pract Exp. 34(6), e6767.

  2. Jena B, Saxena S, Nayak GK, Saba L, Sharma N, Suri JS. Artificial intelligence-based hybrid deep learning models for image classification: the first narrative review. Comput Biol Med. 2021;137: 104803.

    Article  Google Scholar 

  3. Jena B, Dash AK, Nayak GK, Mohapatra P, Saxena S. Image classification for binary classes using deep convolutional neural network: an experimental study. In: Trends of data science and applications. Berlin: Springer; 2021. p. 197–209.

    Chapter  Google Scholar 

  4. Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2015. p. 234–41.

  5. Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, p. 3431–40.

  6. Sinha P, Tuteja M, Saxena S. Medical image segmentation: hard and soft computing approaches. SN Appl Sci. 2020;2(2):1–8.

    Article  Google Scholar 

  7. Jena B, Nayak GK, Saxena S. Comprehensive review of abdominal image segmentation using soft and hard computing approaches. In: 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA). IEEE; 2020. p. 1–5.

  8. Saxena S, et al. Role of artificial intelligence in radiogenomics for cancers in the era of precision medicine. Cancers. 2022;14(12):2860.

    Article  Google Scholar 

  9. FathiKazerooni A, et al. Clinical measures, radiomics, and genomics offer synergistic value in AI-based prediction of overall survival in patients with glioblastoma. Sci Rep. 2022;12(1):1–13.

    Article  Google Scholar 

  10. Jena B, Nayak GK, Saxena S. An empirical study of different machine learning techniques for brain tumor classification and subsequent segmentation using hybrid texture feature. Mach Vis Appl. 2022;33(1):1–16.

    Article  Google Scholar 

  11. Kumari N, Saxena S. Review of brain tumor segmentation and classification. In: 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT). IEEE; 2018. p. 1–6.

  12. Saxena S, Garg A, Mohapatra P. Advanced approaches for medical image segmentation. In: Application of biomedical engineering in neuroscience. Berlin: Springer; 2019. p. 153–72.

    Chapter  Google Scholar 

  13. Wu T, Manogaran AL, Beauchamp J, Waring GL. Drosophila vitelline membrane assembly: a critical role for an evolutionarily conserved cysteine in the “VM domain” of sV23. Dev Biol. 2010;347(2):360–8.

    Article  Google Scholar 

  14. Gerhard S, Funke J, Martel J, Cardona A, Fetter R. Segmented anisotropic ssTEM dataset of neural tissue. figshare, 2013.

  15. Cardona A, et al. An integrated micro-and macroarchitectural analysis of the Drosophila brain by computer-assisted serial section electron microscopy. PLoS Biol. 2010;8(10): e1000502.

    Article  MathSciNet  Google Scholar 

  16. Wang C-W, Gosno EB, Li Y-S. Fully automatic and robust 3D registration of serial-section microscopic images. Sci Rep. 2015;5(1):1–14.

    Google Scholar 

  17. Gerhard S, Andrade I, Fetter RD, Cardona A, Schneider-Mizell CM. Conserved neural circuit structure across Drosophila larval development revealed by comparative connectomics. Elife. 2017;6: e29089.

    Article  Google Scholar 

  18. Pereira S, Pinto A, Alves V, Silva CA. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging. 2016;35(5):1240–51.

    Article  Google Scholar 

  19. Chang J, Zhang X, Ye M, Huang D, Wang P, Yao C. Brain tumor segmentation based on 3D Unet with multi-class focal loss. In: 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE; 2018. p. 1–5.

  20. Skourt BA, El Hassani A, Majda A. Lung CT image segmentation using deep neural networks. Procedia Comput Sci. 2018;127:109–13.

    Article  Google Scholar 

  21. Das S, Nayak GK, Saxena S, Satpathy SC (2021) Effect of learning parameters on the performance of U-Net Model in segmentation of Brain tumor. Multimed Tools Appl. 1–19.

  22. Das S, Bose S, Nayak G K, Satapathy SC, Saxena S (2021) Brain tumor segmentation and overall survival period prediction in glioblastoma multiforme using radiomic features. Concurrency and Computation: Concurr Comput Pract Exp, e6501.

  23. Zhou X-Y, Yang G-Z. Normalization in training U-Net for 2-D biomedical semantic segmentation. IEEE Robot Autom Lett. 2019;4(2):1792–9.

    Article  Google Scholar 

  24. Baheti B, Innani S, Gajre S, Talbar S. Eff-unet: a novel architecture for semantic segmentation in unstructured environment. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020, p. 358–59.

  25. Kolařík M, Burget R, Uher V, Dutta M.K. 3D dense-U-Net for MRI brain tissue segmentation. In: 2018 41st International Conference on Telecommunications and Signal Processing (TSP). IEEE; 2018. p. 1–4.

  26. Falk T, et al. U-Net: deep learning for cell counting, detection, and morphometry. Nat Methods. 2019;16(1):67–70.

    Article  Google Scholar 

  27. Alom MZ, et al. A state-of-the-art survey on deep learning theory and architectures. Electronics. 2019;8(3):292.

    Article  Google Scholar 

  28. Zou KH, et al. Statistical validation of image segmentation quality based on a spatial overlap index1: scientific reports. Acad Radiol. 2004;11(2):178–89.

    Article  Google Scholar 

  29. Khadangi A, Boudier T, Rajagopal V. EM-net: deep learning for electron microscopy image segmentation. In: 2020 25th International Conference on Pattern Recognition (ICPR). IEEE; 2021. p. 31–38.

  30. Suloway C, et al. Automated molecular microscopy: the new Leginon system. J Struct Biol. 2005;151(1):41–60.

    Article  Google Scholar 

  31. Menze BH, et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging. 2014;34(10):1993–2024.

    Article  Google Scholar 

  32. Bakas S, et al. Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci Data. 2017;4(1):1–13.

    Article  Google Scholar 

  33. Bakas S, et al. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. 2018. arXiv preprint http://arxiv.org/abs/02629.

  34. Alqazzaz S, Sun X, Yang X, Nokes L. Automated brain tumor segmentation on multi-modal MR image using SegNet. Comput Vis Media. 2019;5(2):209–19.

    Article  Google Scholar 

  35. Tustison NJ, et al. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging. 2010;29(6):1310–20.

    Article  Google Scholar 

  36. Araujo FH, et al. Deep learning for cell image segmentation and ranking. Comput Med Imaging Graph. 2019;72:13–21.

    Article  Google Scholar 

  37. Shibuya E, Hotta K. Feedback U-Net for cell image segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020, p. 974–5.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, IIIT Bhubaneswar, Bhubaneswar, Odisha, India

    Biswajit Jena, Gopal Krishna Nayak & Sanjay Saxena

  2. Department of Biomedical Engineering, North Eastern Hill University, Shillong, India

    Sudip Paul

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  1. Biswajit Jena
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  2. Gopal Krishna Nayak
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Jena, B., Nayak, G.K., Paul, S. et al. An Exhaustive Analytical Study of U-Net Architecture on Two Diverse Biomedical Imaging Datasets of Electron Microscopy Drosophila ssTEM and Brain MRI BraTS-2021 for Segmentation. SN COMPUT. SCI. 3, 418 (2022). https://doi.org/10.1007/s42979-022-01347-y

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