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FU-Net: fast biomedical image segmentation model based on bottleneck convolution layers

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Abstract

Recently, the introduction of Convolutional Neural Network (CNNs) has advanced the way of solving image segmentation tasks. Semantic image segmentation has considerably benefited from employing various CNN models. The most widely used network in this field is U-Net and its different variations. However, these models require significant number of trainable parameters, floating-point operations per second, and great computational power to be trained. These factors make real-time semantic segmentation in low powered devices very hard. Therefore, in the present paper, we aim to modify particular aspects of the U-Net model to improve its performance through developing a fast U-Net (FU-Net) relying on bottleneck convolution layers in the contraction and expansion paths of the model. The proposed model can be utilized in semantic segmentation applications even on the devices with limited computational power and memory by ensuring the state-of-the-art performance. The amount of memory required by the proposed model is reduced by 23 times when compared with the original U-Net. Moreover, the modifications allowed achieving better performance. In conducted experiments, we assessed the performance of the proposed model on two biomedical image segmentation datasets, namely 2018 Data Science Bowl and ICIS 2018: Skin Lesion Analysis Towards Melanoma Detection. FU-Net demonstrated the state-of-the-art results in biomedical image segmentation, requiring the number of trainable parameters reduced by eight times compared with the original U-Net model. In addition, using bottleneck layers decreased the number of computations, resulting in nearly 30% speed-up at the training, validation and test stages. Furthermore, despite relying on fewer parameters FU-Net achieved a slight improvement of the performance in terms of pixel accuracy, Jaccard index, and dice coefficient evaluation metrics.

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References

  1. Han, C., Duan, Y., Tao, X., Lu, J.: Dense convolutional networks for semantic segmentation. IEEE Access (2019). https://doi.org/10.1109/ACCESS.2019.2908685

    Article  Google Scholar 

  2. Florindo, J.B., Lee, Y.-S., Jun, K., et al.: VisGraphNet: a complex network interpretation of convolutional neural features. Inf Sci (Ny) 543, 296–308 (2020). https://doi.org/10.1016/j.ins.2020.07.050

    Article  Google Scholar 

  3. Liu, X., Deng, Z., Yang, Y.: Recent progress in semantic image segmentation. Artif. Intell. Rev. (2019). https://doi.org/10.1007/s10462-018-9641-3

    Article  Google Scholar 

  4. Greenhalgh, J., Mirmehdi, M.: Real-time detection and recognition of road traffic signs. IEEE Trans. Intell. Transp. Syst. (2012). https://doi.org/10.1109/TITS.2012.2208909

    Article  Google Scholar 

  5. Maldonado-Bascón, S., Lafuente-Arroyo, S., Gil-Jiménez, P., et al.: Road-sign detection and recognition based on support vector machines. IEEE Trans. Intell. Transp. Syst. (2007). https://doi.org/10.1109/TITS.2007.895311

    Article  Google Scholar 

  6. Menze, M., Geiger, A.: Object scene flow for autonomous vehicles. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition (2015)

  7. Matthaei R, Reschka A, Rieken J, et al.: Autonomous driving. In: Handbook of driver assistance systems: basic information, components and systems for active safety and comfort (2015)

  8. Cohen, A., Rivlin, E., Shimshoni, I., Sabo, E.: Memory based active contour algorithm using pixel-level classified images for colon crypt segmentation. Comput. Med. Imaging Graph. (2015). https://doi.org/10.1016/j.compmedimag.2014.12.006

    Article  Google Scholar 

  9. Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging (2016). https://doi.org/10.1109/TMI.2016.2538465

    Article  Google Scholar 

  10. Ibragimov, B., Xing, L.: Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks. Med. Phys. (2017). https://doi.org/10.1002/mp.12045

    Article  Google Scholar 

  11. Kancherla, K., Mukkamala, S.: Early lung cancer detection using nucleus segementation based features. In: Proceedings of the IEEE symposium on computational intelligence in bioinformatics and computational biology, CIBCB 2013—2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 (2013)

  12. Zhou, Y., He, X., Huang, L., et al.: Collaborative learning of semi-supervised segmentation and classification for medical images. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition (2019)

  13. Wei, S., Wu, W., Jeon, G., et al.: Improving resolution of medical images with deep dense convolutional neural network. Concurr. Comput. 32, 1–11 (2020). https://doi.org/10.1002/cpe.5084

    Article  Google Scholar 

  14. Li, X., Song, D., Leng, S.X.: Link between type 2 diabetes and Alzheimer’s disease: From epidemiology to mechanism and treatment. Clin. Interv. Aging 10, 549 (2015)

    Article  Google Scholar 

  15. Liu, F., Chen, L., Lu, L., et al.: Medical image fusion method by using Laplacian pyramid and convolutional sparse representation. Concurr. Comput. 32, 5632 (2019)

    Google Scholar 

  16. Caicedo, J.C., Goodman, A., Karhohs, K.W., et al.: Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl. Nat. Methods (2019). https://doi.org/10.1038/s41592-019-0612-7

    Article  Google Scholar 

  17. Otsu, N.: Threshold selection method from gray-level histograms. IEEE Trans. Syst. Man. Cybern. (1979). https://doi.org/10.1109/TSMC.1979.4310076

    Article  Google Scholar 

  18. Malpica, N., De Solórzano, C.O., Vaquero, J.J., et al.: Applying watershed algorithms to the segmentation of clustered nuclei. Cytometry (1997). https://doi.org/10.1002/(SICI)1097-0320(19970801)28:4%3c289::AID-CYTO3%3e3.0.CO;2-7

    Article  Google Scholar 

  19. Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. (2001). https://doi.org/10.1109/83.902291

    Article  MATH  Google Scholar 

  20. Mansouri, B., Housewright, C.D.: The treatment of actinic keratoses—the rule rather than the exception. JAMA Dermatol. 153, 1200 (2017). https://doi.org/10.1001/jamadermatol.2017.3395

    Article  Google Scholar 

  21. Institute NNC: Cancer facts & figures 2020. CA Cancer J Clin 66, 366–367 (2020)

    Google Scholar 

  22. Li, Y., Shen, L.: Skin lesion analysis towards melanoma detection using deep learning network. Sens (Switzerl) (2018). https://doi.org/10.3390/s18020556

    Article  Google Scholar 

  23. Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Lecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2015)

  24. Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition (2015)

  25. MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability (1967)

  26. Gómez, D.D., Butakoff, C., Ersbøll, B.K., Stoecker, W.: Independent histogram pursuit for segmentation of skin lesions. IEEE Trans. Biomed. Eng. (2008). https://doi.org/10.1109/TBME.2007.910651

    Article  Google Scholar 

  27. Zheng, L., Li, G., Bao, Y.: Improvement of grayscale image 2D maximum entropy threshold segmentation method. In: 2010 international conference on logistics systems and intelligent management, ICLSIM 2010 (2010)

  28. Barghout, L.: Visual taxometric approach to image segmentation using fuzzy-spatial taxon cut yields contextually relevant regions. In: Communications in computer and information science (2014)

  29. Garnavi, R., Aldeen, M., Bailey, J.: Computer-aided diagnosis of melanoma using border- and wavelet-based texture analysis. IEEE Trans. Inf. Technol. Biomed. (2012). https://doi.org/10.1109/TITB.2012.2212282

    Article  Google Scholar 

  30. Emre Celebi, M., Wen, Q., Iyatomi, H., et al.: A state-of-the-art survey on lesion border detection in dermoscopy images. Dermoscopy Image Anal. 10, 97–129 (2015)

    Google Scholar 

  31. Drozdzal, M., Vorontsov, E., Chartrand, G., et al.: The importance of skip connections in biomedical image segmentation. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) (2016)

  32. Yu, L., Chen, H., Dou, Q., et al.: Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans. Med. Imaging (2017). https://doi.org/10.1109/TMI.2016.2642839

    Article  Google Scholar 

  33. Ibtehaz, N., Rahman, M.S.: MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Netw. (2020). https://doi.org/10.1016/j.neunet.2019.08.025

    Article  Google Scholar 

  34. Salido, J.A.A., Ruiz, C.: Using deep learning for melanoma detection in dermoscopy images. Int. J. Mach. Learn. Comput. 8, 61–68 (2018). https://doi.org/10.18178/ijmlc.2018.8.1.664

    Article  Google Scholar 

  35. Bisla, D., Choromanska, A., Berman, R.S., et al.: Towards automated melanoma detection with deep learning: Data purification and augmentation. In: IEEE computer society conference on computer vision and pattern recognition workshops (2019)

  36. Smochina, C., Manta, V., Kropatsch, W.: Semantic segmentation of microscopic images using a morphological hierarchy. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2011)

  37. Chang, S.W., Liao, S.W.: KUnet: microscopy image segmentation with deep unet based convolutional networks. Conf. Proc. IEEE Int. Conf. Syst. Man Cybern. 2019, 3561–3566 (2019). https://doi.org/10.1109/SMC.2019.8914048

    Article  Google Scholar 

  38. Isaksson, J., Arvidsson, I., Aastrom, K., Heyden, A.: Semantic segmentation of microscopic images of H&E stained prostatic tissue using CNN. In: Proceedings of the international joint conference on neural networks (2017)

  39. Minaee, S., Boykov, Y., Porikli, F., et al.: Image segmentation using deep learning: a survey, pp. 1–23 (2020). https://arxiv.org/abs/2001.05566

  40. Shen, D., Wu, G., Suk, H.-I.: Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. (2017). https://doi.org/10.1146/annurev-bioeng-071516-044442

    Article  Google Scholar 

  41. Rizwan, I., Haque, I., Neubert, J.: Deep learning approaches to biomedical image segmentation. Informatics Med. Unlocked 18, 100297 (2020). https://doi.org/10.1016/j.imu.2020.100297

    Article  Google Scholar 

  42. Hesamian, M.H., Jia, W., He, X., Kennedy, P.: Deep learning techniques for medical image segmentation: achievements and challenges. J. Dig. Imaging (2019). https://doi.org/10.1007/s10278-019-00227-x

    Article  Google Scholar 

  43. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: 32nd international conference on machine learning, ICML 2015 (2015)

  44. He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision (2015)

  45. Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. In: 3rd International conference on learning representations, ICLR 2015—conference track proceedings (2015)

  46. Fu, S., Lu, L., Li, H., Li, Z., Wu, W., Paul, A., Jeon, G., Yang, X.: A real-time super-resolution method based on convolutional neural networks. Circ. Syst. Signal Proc. 39(2), 805–817 (2020)

    Article  Google Scholar 

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Acknowledgements

This study was supported by the BK21 Plus project (SW Human Resource Development Program for Supporting Smart Life) funded by the Ministry of Education, School of Computer Science and Engineering, Kyungpook National University, Korea (21A20131600005).

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This research received no external funding.

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Authors and Affiliations

  1. The School of Computer Science and Engineering, Kyungpook National University, Daegu, 41566, South Korea

    Bekhzod Olimov, Karshiev Sanjar, Sadia Din, Awaise Ahmad, Anand Paul & Jeonghong Kim

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  1. Bekhzod Olimov
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  2. Karshiev Sanjar
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Contributions

Conceptualization, OB; data curation, KS; Formal analysis, SD; Funding acquisition, JK; Investigation, AA; Methodology, OB; Project administration, JK; Resources, SK; Software, OB; Validation, AP; Writing—original draft, OB; Writing—review, JK.

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Correspondence to Jeonghong Kim.

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Olimov, B., Sanjar, K., Din, S. et al. FU-Net: fast biomedical image segmentation model based on bottleneck convolution layers. Multimedia Systems 27, 637–650 (2021). https://doi.org/10.1007/s00530-020-00726-w

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