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Ejection Fraction estimation using deep semantic segmentation neural network

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Abstract

The Ejection Fraction value denotes how much blood is pumped out of the heart to different parts of the body. It is a routine clinical procedure in heart function assessment, where the left ventricle of the heart has to be manually outlined by doctors in clinical settings to measure the Ejection Fraction value which is time-consuming and highly varies by the observer. Most of the state-of-the-art automated Ejection Fraction estimation methods applied statistical or neural network models to generic and expensive clinical procedures like 3D ultrasound, MRI, and CT imaging. However, 2D echocardiography is a specialized diagnosis method that is inexpensive and routinely used in clinical settings to diagnose heart diseases. This paper proposed an automated Ejection Fraction estimation system from 2D echocardiography images using deep semantic segmentation neural networks. Two parallel pipelines of deep semantic segmentation neural network models have been proposed for efficient left ventricle (LV) segmentation in its systolic (contracted) and diastolic (expanded) states. The three different semantic segmentation neural networks, namely UNet, ResUNet, and Deep ResUNet, have been implemented in those parallel pipelines, and the performance of the proposed model has been studied on a standard 2D echocardiography data set. The most accurate model among the three achieved a Dice score of 82.1% and 86.5% in LV segmentation on end systole and end diastole states, respectively. The Ejection Fraction value is then determined by applying the volume measurement formula to the output of the left ventricle segmentation network. Therefore, the proposed automated Ejection Fraction system can be used in clinical settings to remove the eyeball estimation practice and reduce the inter-observer variability problem.

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Data availability statement

The data used in the manuscript is available publicly.

References

  1. Ahmed WS (2020) The impact of filter size and number of filters on classification accuracy in CNN. In 2020 International Conference on Computer Science and Software Engineering (CSASE), pp. 88–93. IEEE

  2. Md Alam GR, Abedin SF, Al Ameen M, Hong CS (2016) Web of objects based ambient assisted living framework for emergency psychiatric state prediction. Sensors 16(9):1431

    Article  Google Scholar 

  3. Md Alam GR, Abedin SF, Il Moon S, Talukder A, Hong CS (2019) Healthcare IoT-based affective state mining using a deep convolutional neural network. IEEE Access 7:75189–75202

    Article  Google Scholar 

  4. Bamira D, Picard MH (2018) Imaging: echocardiology—assessment of cardiac structure and function. Encycl Cardiovasc Res Med. https://doi.org/10.1016/b978-0-12-809657-4.10953-6

    Article  Google Scholar 

  5. Barry-Straume J, Tschannen A, Engels DW, Fine E (2018) An evaluation of training size impact on validation accuracy for optimized convolutional neural networks. SMU Data Sci Rev 1(4):12

    Google Scholar 

  6. Benyounes N, Van Der Vynckt C, Tibi T, Iglesias A, Gout O, Lang S, Salomon L (2020) Left ventricular end diastolic volume and ejection fraction calculation: correlation between three echocardiographic methods. Cardiol Res Pract 2020:1–7. https://doi.org/10.1155/2020/8076582

    Article  Google Scholar 

  7. Bernard O, Bosch JG, Heyde B, Alessandrini M, Barbosa D, Camarasu-Pop S, Cervenansky F, Valette S, Mirea O, Bernier M et al (2015) Standardized evaluation system for left ventricular segmentation algorithms in 3d echocardiography. IEEE Trans Med Imag 35(4):967–977

    Article  Google Scholar 

  8. Bernard O, Heyde B, Alessandrini M, Barbosa D, Camarasu-Pop S, Cervenansky F, Valette S, Mirea OC, Galli E, Geleijnse M et al. (2014) Challenge on endocardial three-dimensional ultrasound segmentation (cetus). In: Proceedings MICCAI Challenge on Echocardiographic Three-Dimensional Ultrasound Segmentation (CETUS), pp 1–8

  9. Birsan T, Tiba D (2005) One hundred years since the introduction of the set distance by dimitrie pompeiu. In: IFIP Conference on System Modeling and Optimization. Springer, pp 35–39

  10. Burçak KC, Baykan ÖK, Uğuz H (2021) A new deep convolutional neural network model for classifying breast cancer histopathological images and the hyperparameter optimisation of the proposed model. J Supercomput 77(1):973–989

  11. Cai L, Gao J, Zhao D (2020) A review of the application of deep learning in medical image classification and segmentation. Ann Transl Med 8(11):713–713. https://doi.org/10.21037/atm.2020.02.44

    Article  Google Scholar 

  12. Carneiro G, Nascimento J, Freitas A (2012) The segmentation of the left ventricle of the heart from ultrasound data using deep learning architectures and derivative-based search methods. IEEE Trans Image Process 21:968–982

    Article  MathSciNet  MATH  Google Scholar 

  13. Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848

    Article  Google Scholar 

  14. Chu Z, Tian T, Feng R, Wang L (2019) Sea-land segmentation with res-unet and fully connected crf. In: IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, pp. 3840–3843. IEEE

  15. Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3d u-net: learning dense volumetric segmentation from sparse annotation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, pp 424–432

  16. Diakogiannis FI, Waldner F, Caccetta P, Wu C (2020) Resunet-a: a deep learning framework for semantic segmentation of remotely sensed data. ISPRS J Photogramm Remote Sens 162:94–114

    Article  Google Scholar 

  17. Do L-N, Yang H-J, Nguyen H-D, Kim S-H, Lee G-S, Na I-S (2021) Deep neural network-based fusion model for emotion recognition using visual data. J Supercomput 77(10):10773–10790. https://doi.org/10.1007/s11227-021-03690-y

    Article  Google Scholar 

  18. Drozdzal M, Vorontsov E, Chartrand G, Kadoury S, Pal C (2016) The importance of skip connections in biomedical image segmentation. In: Deep learning and data labeling for medical applications. Springer, pp 179–187

  19. Eelbode T, Bertels J, Berman M, Vandermeulen D, Maes F, Bisschops R, Blaschko MB (2020) Optimization for medical image segmentation: theory and practice when evaluating with dice score or jaccard index. IEEE Trans Med Imag 39(11):3679–3690

    Article  Google Scholar 

  20. Garcia-Garcia A, Orts-Escolano S, Oprea S, Villena-Martinez V, Garcia-Rodriguez J (2017) A review on deep learning techniques applied to semantic segmentation. arXiv:1704.06857

  21. He K, Sun J (2015) Convolutional neural networks at constrained time cost. In: Proceedings of the IEEE Conference on Computer vVision and Pattern Recognition, pp 5353–5360

  22. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 770–778

  23. Jiang H, Diao Z, Yao Y-D (2021) Deep learning techniques for tumor segmentation: a review. J Supercomput. https://doi.org/10.1007/s11227-021-03901-6

    Article  Google Scholar 

  24. Jiang M, Spence JD, Chiu B (2020) Segmentation of 3d ultrasound carotid vessel wall using u-net and segmentation average network. In: 2020 42nd annual international conference of the IEEE engineering in medicine & biology society (EMBC). IEEE, pp 2043–2046

  25. Jifara W, Jiang F, Rho S, Cheng M, Liu S (2019) Medical image denoising using convolutional neural network: a residual learning approach. J Supercomput 75(2):704–718

    Article  Google Scholar 

  26. Jo J, Jeong S, Kang P (2020) Benchmarking gpu-accelerated edge devices. In: 2020 IEEE international conference on big data and smart computing (BigComp), pp 117–120. IEEE

  27. Kadry S, Rajinikanth V, Taniar D, Damaševičius R, Valencia XPB (2021) Automated segmentation of leukocyte from hematological images-a study using various cnn schemes. J Supercomput, pp 1–21

  28. Kosaraju A, Goyal A, Grigorova Y, Makaryus AN (2020) Left ventricular ejection fraction.[updated 2020 may 5]. StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing

  29. Krishnaswamy D, Hareendranathan AR, Suwatanaviroj T, Becher H, Noga M, Punithakumar K (2018) A semi-automated method for measurement of left ventricular volumes in 3d echocardiography. IEEE Access 6:16336–16344

    Article  Google Scholar 

  30. Lan Y, Zhang X (2020) Real-time ultrasound image despeckling using mixed-attention mechanism based residual unet. IEEE Access 8:195327–195340

    Article  Google Scholar 

  31. Lebenberg J, Buvat I, Lalande A, Clarysse P, Casta C, Cochet A, Constantinidés C, Cousty J, De Cesare A, Jehan-Besson S et al (2012) Nonsupervised ranking of different segmentation approaches: application to the estimation of the left ventricular ejection fraction from cardiac cine mri sequences. IEEE Trans Med Imag 31(8):1651–1660

    Article  Google Scholar 

  32. Liu Y-H, Sandoval V, Sinusas AJ (2013) Potential impact of hybrid czt spect/ct imaging on estimation accuracy of left ventricular volumes and ejection fraction: a phantom study. In: 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (2013 NSS/MIC), pp 1–5. IEEE

  33. Oktay O, Ferrante E, Kamnitsas K, Heinrich M, Bai W, Caballero J, Cook SA, De Marvao A, Dawes T, O’Regan DP et al (2017) Anatomically constrained neural networks (acnns): application to cardiac image enhancement and segmentation. IEEE Trans Med Imag 37(2):384–395

    Article  Google Scholar 

  34. Pombo JF, Troy BL, Russell ROJR (1971) Left ventricular volumes and ejection fraction by echocardiography. Circulation 43(4):480–490

    Article  Google Scholar 

  35. Ray V, Goyal A (2015) Image based sub-second fast fully automatic complete cardiac cycle left ventricle segmentation in multi frame cardiac mri images using pixel clustering and labelling. In: 2015 Eighth International Conference on Contemporary Computing (IC3), pp 248–252. IEEE

  36. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, pp 234–241

  37. Rucklidge WJ (1997) Efficiently locating objects using the hausdorff distance. Int J Comput Vis 24(3):251–270

    Article  Google Scholar 

  38. Shen Y, Zhang H, Fan Y, Lee AP, Xu L (2021) Smart health of ultrasound telemedicine based on deeply represented semantic segmentation. IEEE Internet Things J 8(23):16770–16778

    Article  Google Scholar 

  39. Shi S, Wang Q, Xu P, Chu X (2016) Benchmarking state-of-the-art deep learning software tools. In: 2016 7th International Conference on Cloud Computing and Big Data (CCBD). IEEE, pp 99–104

  40. Smistad E, Østvik A et al. (2017) 2d left ventricle segmentation using deep learning. In: 2017 IEEE International Ultrasonics Symposium (IUS), pp 1–4. IEEE

  41. Smistad E, Østvik A, Salte IM, Melichova D, Nguyen TM, Haugaa K, Brunvand H, Edvardsen T, Leclerc S, Bernard O et al (2020) Real-time automatic ejection fraction and foreshortening detection using deep learning. IEEE Trans Ultrasonics Ferroelectr Freq Control 67(12):2595–2604

    Article  Google Scholar 

  42. Uchida S, Ide S, Iwana BK, Zhu A (2016) A further step to perfect accuracy by training cnn with larger data. In: 2016 15th international conference on frontiers in handwriting recognition (ICFHR). IEEE, pp 405–410

  43. Wang J, Lv P, Wang H, Shi C (2021) Sar-u-net: squeeze-and-excitation block and atrous spatial pyramid pooling based residual u-net for automatic liver ct segmentation. arXiv:2103.06419

  44. Zhang Y, Mehta S, Caspi A (2021) Rethinking semantic segmentation evaluation for explainability and model selection. arXiv:2101.08418

Download references

Acknowledgements

The authors are grateful to the Deanship of Scientific Research at King Saud University for funding this work through the Vice Deanship of Scientific Research Chairs: Research Chair of New Emerging Technologies and 5G Networks and Beyond.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, BRAC University, Mohakhali, Dhaka, 1212, Bangladesh

    Md. Golam Rabiul Alam, Abde Musavvir Khan, Myesha Farid Shejuty, Syed Ibna Zubayear & Md. Nafis Shariar

  2. Advanced Manufacturing and Industry 4.0 Center, King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia

    Meteb Altaf

  3. Research Chair of New Emerging Technologies and 5G Networks and Beyond, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia

    Mohammad Mehedi Hassan

  4. Research Chair of New Emerging Technologies and 5G Networks and Beyond, Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia

    Salman A. AlQahtani

  5. Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia

    Mohammad Mehedi Hassan & Ahmed Alsanad

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  1. Md. Golam Rabiul Alam
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Alam, M.G.R., Khan, A.M., Shejuty, M.F. et al. Ejection Fraction estimation using deep semantic segmentation neural network. J Supercomput 79, 27–50 (2023). https://doi.org/10.1007/s11227-022-04642-w

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