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Segmentation of shoulder muscle MRI using a new Region and Edge based Deep Auto-Encoder

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Abstract

Automatic segmentation of shoulder muscle MRI is challenging due to the high variation in muscle size, shape, texture, and spatial position of tears. Manual segmentation of tear and muscle portion is hard, time-consuming, and subjective to pathological expertise. This work proposes a new Region and Edge-based Deep Auto-Encoder (RE-DAE) for shoulder muscle MRI segmentation. The proposed RE-DAE harmoniously employs average and max-pooling operations in the Convolutional encoder and decoder blocks. The DAE’s region and edge-based segmentation encourage the network to extract homogenous and anatomical information, respectively. These two concepts, systematically combined in a DAE, generate a discriminative and sparse hybrid feature space (exploiting both region homogeneity and boundaries). Moreover, the concept of static attention is exploited in the proposed RE-DAE that helps effectively learn the tear region. The performances of the proposed RE-DAE architectures have been tested using a 3D MRI shoulder muscle dataset using the hold-out cross-validation technique. The MRI data has been collected from the Korea University Anam Hospital, Seoul, South Korea. Experimental comparisons have been conducted by employing innovative custom-made and existing pre-trained CNN architectures using transfer learning and fine-tuning. Objective evaluation on the muscle datasets using the proposed SA-RE-DAE showed a dice similarity of 85.58% and 87.07%, an accuracy of 81.57% and 95.58% for tear and muscle regions, respectively. The high visual quality and the objective result suggest that the proposed SA-RE-DAE can correctly segment tear and muscle regions in shoulder muscle MRI for better clinical decisions.

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References

  1. Ahmad P, Jin H, Qamar S et al (2021) RD2A: densely connected residual networks using ASPP for brain tumor segmentation. Multimed Tools Appl 80:27069–27094

    Article  Google Scholar 

  2. Ahmed U, Khan A, Khan SH et al (2019) Transfer learning and meta classification based deep churn prediction system for telecom industry. arXiv

  3. Alipour N, Hasanzadeh RPR (2021) Superpixel-based brain tumor segmentation in MR images using an extended local fuzzy active contour model. Multimed Tools Appl 80:8835–8859

    Article  Google Scholar 

  4. Badrinarayanan V, Mishra B, Cipolla R (2015) Understanding symmetries in deep networks

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

  6. Biltz NK, Meyer GA (2017) A novel method for the quantification of fatty infiltration in skeletal muscle. Skelet Muscle 7:1

    Article  Google Scholar 

  7. Bresson X, Esedoglu S, Vandergheynst P et al (2007) Fast global minimization of the active contour/snake model. J Math Imaging Vis 28:151–167

    Article  MathSciNet  Google Scholar 

  8. Chahal ES, Patel A, Gupta A et al (2021) Unet based Xception model for prostate cancer segmentation from MRI images. Multimed Tools Appl. https://doi.org/10.1007/s11042-021-11334-9

  9. Çiçek Ö, Abdulkadir A, Lienkamp SS et al (2016) 3D U-net: Learning dense volumetric segmentation from sparse annotation. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 9901 LNCS:424–432

    Google Scholar 

  10. Conze P-H, Brochard S, Burdin V et al (2020) Healthy versus pathological learning transferability in shoulder muscle MRI segmentation using deep convolutional encoder-decoders. Comput Med Imaging Graph 83:101733. https://doi.org/10.1016/j.compmedimag.2020.101733

    Article  Google Scholar 

  11. DEPALMA AF (1963) Surgical anatomy of the rotator cuff and the natural history of degenerative periarthritis. Surg Clin North Am 43:1507–1520

  12. Devi D, Namasudra S, Kadry S (2020) A boosting-aided adaptive cluster-based undersampling approach for treatment of class imbalance problem. Int J Data Warehous Min 16:60–86. https://doi.org/10.4018/IJDWM.2020070104

  13. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. 32nd Int. Conf. Mach. Learn. ICML 2015 1, pp 448–456

  14. Javed SG, Majid A, Mirza AM, Khan A (2016) Multi-denoising based impulse noise removal from images using robust statistical features and genetic programming. Multimed Tools Appl 75:5887–5916

    Article  Google Scholar 

  15. Jiang J, Liu X, Zhang K et al (2017) Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network. Biomed Eng Online 16:132

    Article  Google Scholar 

  16. Khagi B, Kwon GR (2018) Pixel-label-based segmentation of cross-sectional brain MRI using simplified segnet architecture-based CNN. J Healthc Eng 2018:2018

    Article  Google Scholar 

  17. Khan SH, Sohail A, Khan A, Lee YS (2020) Classification and region analysis of COVID-19 infection using lung. CT images and deep convolutional neural networks

  18. Khan SH, Yousaf MH, Murtaza F, Velastin S (2020) Passenger detection and counting for public transport system. NED Univ J Res XVII:35–46

  19. Khan SH, Sohail A, Zafar MM, Khan A (2021) Coronavirus disease analysis using chest X-ray images and a novel deep convolutional neural network. Photodiagnosis Photodyn Ther 35:102473. https://doi.org/10.1016/j.pdpdt.2021.102473

    Article  Google Scholar 

  20. Khan SH, Sohail A, Khan A et al (2021) COVID-19 detection in chest X-ray images using deep boosted hybrid learning. Comput Biol Med 137:104816

    Article  Google Scholar 

  21. Khan SH, Sohail A, Khan A, Lee YS (2022) COVID-19 detection in chest X-ray images using a new channel boosted CNN. Diagnostics 12:267

    Article  Google Scholar 

  22. Khan A, Hussain Khan S, Saif M et al A survey of deep learning techniques for the analysis of COVID-19 and their usability for detecting omicron

  23. Kim S, Lee D, Park S et al (2017) Automatic segmentation of supraspinatus from MRI by internal shape fitting and autocorrection. Comput Methods Programs Biomed 140:165–174

    Article  Google Scholar 

  24. Kim JY, Ro K, You S et al (2019) Development of an automatic muscle atrophy measuring algorithm to calculate the ratio of supraspinatus in supraspinous fossa using deep learning. Comput Methods Programs Biomed 182:105063

    Article  Google Scholar 

  25. Kollias D, Tagaris A, Stafylopatis A et al (2018) Deep neural architectures for prediction in healthcare. Complex Intell Syst 4:119–131

    Article  Google Scholar 

  26. Kumar GA, Sridevi PV (2021) E-fuzzy feature fusion and thresholding for morphology segmentation of brain MRI modalities. Multimed Tools Appl 80:19715–19735

    Article  Google Scholar 

  27. Kumar P, Nagar P, Arora C, Gupta A (2018) U-segnet: fully convolutional neural network based automated brain tissue segmentation tool. arXiv

  28. Lee H, Troschel FM, Tajmir S et al (2017) Pixel-level deep segmentation: artificial intelligence quantifies muscle on computed tomography for body morphometric analysis. J Digit Imaging 30:487–498

  29. Li MW, Wang YT, Geng J, Hong WC (2021) Chaos cloud quantum bat hybrid optimization algorithm. Nonlinear Dyn 103:1167–1193

    Article  Google Scholar 

  30. Mandić M, Rullman E, Widholm P et al (2020) Automated assessment of regional muscle volume and hypertrophy using MRI. Sci Rep 10:2239

    Article  Google Scholar 

  31. Pavel M, Jimison HB, Wactlar HD et al (2013) The role of technology and engineering models in transforming healthcare. IEEE Rev Biomed Eng 6:156–177

    Article  Google Scholar 

  32. Pons C, Sheehan FT, Im HS et al (2017) Shoulder muscle atrophy and its relation to strength loss in obstetrical brachial plexus palsy. Clin Biomech 48:80–87

    Article  Google Scholar 

  33. Qureshi AS, Khan A (2018) Adaptive transfer learning in deep neural networks: wind power prediction using knowledge transfer from region to region and between different task domains. arXiv

  34. Qureshi AS, Khan A, Zameer A, Usman A (2017) Wind power prediction using deep neural network based meta regression and transfer learning. Appl Soft Comput J 58:742–755

    Article  Google Scholar 

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

    Google Scholar 

  36. Shelhamer E, Long J, Darrell T (2017) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39:640–651

  37. Schlemper J, Oktay O, Schaap M et al (2019) Attention gated networks: learning to leverage salient regions in medical images. Med Image Anal 53:197–207

  38. Slabaugh MA, Friel NA, Karas V et al (2012) Interobserver and intraobserver reliability of the goutallier classification using magnetic resonance imaging. Am J Sports Med 40:1728–1734

  39. Singh LK, Pooja, Garg H et al (2021) An analytical study on machine learning techniques, pp 137–157

  40. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., pp 1–14

  41. Tian Y, Duan F, Zhou M, Wu Z (2013) Active contour model combining region and edge information. Mach Vis Appl 24:47–61

    Article  Google Scholar 

  42. van G STJJM, L DMJ et al (2017) Deep Learning for fully-automated localization and segmentation of rectal cancer on multiparametric MR. Sci Rep 7:5301

  43. Vedaldi A, Lenc K (2015) MatConvNet: Convolutional neural networks for MATLAB. MM 2015 - Proc. 2015 ACM Multimed. Conf., pp 689–692

  44. Ward AD, Hamarneh G, Ashry R, Schweitzer ME (2007) 3D shape analysis of the supraspinatus muscle. Acad Radiol 14:1229–1241

  45. Ward AD, Hamarneh G, Ashry R, Schweitzer ME (2007) 3D shape analysis of the supraspinatus muscle. A clinical study of the relationship between shape and pathology. Acad Radiol 14:1229–1241

  46. Zafar MM, Rauf Z, Sohail A et al (2022) Detection of tumour infiltrating lymphocytes in CD3 and CD8 stained histopathological images using a two-phase deep CNN. Photodiagnosis Photodyn Ther 37:102676. https://doi.org/10.1016/j.pdpdt.2021.102676

    Article  Google Scholar 

  47. Zhang Z, Hong WC (2021) Application of variational mode decomposition and chaotic grey wolf optimizer with support vector regression for forecasting electric loads. Knowl Based Syst 228:107297

    Article  Google Scholar 

  48. Zhang C, Hua Q-Q, Chu Y-Y, Wang P-W (2021) Liver tumor segmentation using 2.5D UV-Net with multi-scale convolution. Comput Biol Med 133:104424. https://doi.org/10.1016/j.compbiomed.2021.104424

    Article  Google Scholar 

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Acknowledgements

This work was conducted with the support of the PIEAS IT endowment fund under the Pakistan Higher Education Commission (HEC). This study was also supported by the research grant of National Research Foundation of Korea (2017R1A2B2005065).As well as, we thank Pattern Recognition Lab (PR-Lab) and Pakistan Institute of Engineering, and Applied Sciences (PIEAS), for providing necessary computational resources and a healthy research environment.

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Authors and Affiliations

  1. Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, 45650, Pakistan

    Saddam Hussain Khan & Asifullah Khan

  2. Department of Computer Systems Engineering, University of Engineering and Applied Sciences (UEAS), Swat, 19060, Pakistan

    Saddam Hussain Khan

  3. PIEAS Artificial Intelligence Center (PAIC), Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, 45650, Pakistan

    Saddam Hussain Khan & Asifullah Khan

  4. Center for Mathematical Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, 45650, Pakistan

    Asifullah Khan

  5. Department of Biomedical Engineering, College of Medical Science, Catholic University of Daegu, Daegu, South Korea

    Yeon Soo Lee

  6. Department of Computer Science, Air University, Islamabad, Pakistan

    Mehdi Hassan

  7. Department of Orthopaedic Surgery, College of Medicine, Korea University, Seoul, Korea

    Woong Kyo Jeong

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  1. Saddam Hussain Khan
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  2. Asifullah Khan
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Correspondence to Asifullah Khan.

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Khan, S.H., Khan, A., Lee, Y.S. et al. Segmentation of shoulder muscle MRI using a new Region and Edge based Deep Auto-Encoder. Multimed Tools Appl 82, 14963–14984 (2023). https://doi.org/10.1007/s11042-022-14061-x

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