{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:49:16Z","timestamp":1778082556483,"version":"3.51.4"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,7,2]],"date-time":"2022-07-02T00:00:00Z","timestamp":1656720000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,7,2]],"date-time":"2022-07-02T00:00:00Z","timestamp":1656720000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100011665","name":"Deanship of Scientific Research, King Saud University","doi-asserted-by":"publisher","award":["Research Chair"],"award-info":[{"award-number":["Research Chair"]}],"id":[{"id":"10.13039\/501100011665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1007\/s11227-022-04642-w","type":"journal-article","created":{"date-parts":[[2022,7,2]],"date-time":"2022-07-02T08:05:52Z","timestamp":1656749152000},"page":"27-50","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Ejection Fraction estimation using deep semantic segmentation neural network"],"prefix":"10.1007","volume":"79","author":[{"given":"Md. Golam Rabiul","family":"Alam","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abde Musavvir","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Myesha Farid","family":"Shejuty","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Syed Ibna","family":"Zubayear","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md. Nafis","family":"Shariar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meteb","family":"Altaf","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3479-3606","authenticated-orcid":false,"given":"Mohammad Mehedi","family":"Hassan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Salman A.","family":"AlQahtani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed","family":"Alsanad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,2]]},"reference":[{"key":"4642_CR1","doi-asserted-by":"crossref","unstructured":"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\u201393. IEEE","DOI":"10.1109\/CSASE48920.2020.9142089"},{"issue":"9","key":"4642_CR2","doi-asserted-by":"publisher","first-page":"1431","DOI":"10.3390\/s16091431","volume":"16","author":"GR Md Alam","year":"2016","unstructured":"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","journal-title":"Sensors"},{"key":"4642_CR3","doi-asserted-by":"publisher","first-page":"75189","DOI":"10.1109\/ACCESS.2019.2919995","volume":"7","author":"GR Md Alam","year":"2019","unstructured":"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\u201375202","journal-title":"IEEE Access"},{"key":"4642_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/b978-0-12-809657-4.10953-6","author":"D Bamira","year":"2018","unstructured":"Bamira D, Picard MH (2018) Imaging: echocardiology\u2014assessment of cardiac structure and function. Encycl Cardiovasc Res Med. https:\/\/doi.org\/10.1016\/b978-0-12-809657-4.10953-6","journal-title":"Encycl Cardiovasc Res Med"},{"issue":"4","key":"4642_CR5","first-page":"12","volume":"1","author":"J Barry-Straume","year":"2018","unstructured":"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","journal-title":"SMU Data Sci Rev"},{"key":"4642_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/8076582","volume":"2020","author":"N Benyounes","year":"2020","unstructured":"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\u20137. https:\/\/doi.org\/10.1155\/2020\/8076582","journal-title":"Cardiol Res Pract"},{"issue":"4","key":"4642_CR7","doi-asserted-by":"publisher","first-page":"967","DOI":"10.1109\/TMI.2015.2503890","volume":"35","author":"O Bernard","year":"2015","unstructured":"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\u2013977","journal-title":"IEEE Trans Med Imag"},{"key":"4642_CR8","doi-asserted-by":"crossref","unstructured":"Bernard O, Heyde B, Alessandrini M, Barbosa D, Camarasu-Pop S, Cervenansky F, Valette S, Mirea OC, Galli E, Geleijnse M et\u00a0al. (2014) Challenge on endocardial three-dimensional ultrasound segmentation (cetus). In: Proceedings MICCAI Challenge on Echocardiographic Three-Dimensional Ultrasound Segmentation (CETUS), pp 1\u20138","DOI":"10.54294\/j78w0v"},{"key":"4642_CR9","doi-asserted-by":"crossref","unstructured":"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\u201339","DOI":"10.1007\/0-387-33006-2_4"},{"key":"4642_CR10","doi-asserted-by":"crossref","unstructured":"Bur\u00e7ak KC, Baykan \u00d6K, U\u011fuz 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\u2013989","DOI":"10.1007\/s11227-020-03321-y"},{"issue":"11","key":"4642_CR11","doi-asserted-by":"publisher","first-page":"713","DOI":"10.21037\/atm.2020.02.44","volume":"8","author":"L Cai","year":"2020","unstructured":"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\u2013713. https:\/\/doi.org\/10.21037\/atm.2020.02.44","journal-title":"Ann Transl Med"},{"key":"4642_CR12","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1109\/TIP.2011.2169273","volume":"21","author":"G Carneiro","year":"2012","unstructured":"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\u2013982","journal-title":"IEEE Trans Image Process"},{"issue":"4","key":"4642_CR13","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L-C Chen","year":"2017","unstructured":"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\u2013848","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4642_CR14","doi-asserted-by":"crossref","unstructured":"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\u20133843. IEEE","DOI":"10.1109\/IGARSS.2019.8900625"},{"key":"4642_CR15","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek \u00d6, 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\u2013432","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"4642_CR16","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","volume":"162","author":"FI Diakogiannis","year":"2020","unstructured":"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\u2013114","journal-title":"ISPRS J Photogramm Remote Sens"},{"issue":"10","key":"4642_CR17","doi-asserted-by":"publisher","first-page":"10773","DOI":"10.1007\/s11227-021-03690-y","volume":"77","author":"L-N Do","year":"2021","unstructured":"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\u201310790. https:\/\/doi.org\/10.1007\/s11227-021-03690-y","journal-title":"J Supercomput"},{"key":"4642_CR18","doi-asserted-by":"crossref","unstructured":"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\u2013187","DOI":"10.1007\/978-3-319-46976-8_19"},{"issue":"11","key":"4642_CR19","doi-asserted-by":"publisher","first-page":"3679","DOI":"10.1109\/TMI.2020.3002417","volume":"39","author":"T Eelbode","year":"2020","unstructured":"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\u20133690","journal-title":"IEEE Trans Med Imag"},{"key":"4642_CR20","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1016\/j.asoc.2018.05.018"},{"key":"4642_CR21","doi-asserted-by":"crossref","unstructured":"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\u20135360","DOI":"10.1109\/CVPR.2015.7299173"},{"key":"4642_CR22","doi-asserted-by":"crossref","unstructured":"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\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"4642_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-021-03901-6","author":"H Jiang","year":"2021","unstructured":"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","journal-title":"J Supercomput"},{"key":"4642_CR24","doi-asserted-by":"crossref","unstructured":"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\u20132046","DOI":"10.1109\/EMBC44109.2020.9175975"},{"issue":"2","key":"4642_CR25","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1007\/s11227-017-2080-0","volume":"75","author":"W Jifara","year":"2019","unstructured":"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\u2013718","journal-title":"J Supercomput"},{"key":"4642_CR26","doi-asserted-by":"crossref","unstructured":"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\u2013120. IEEE","DOI":"10.1109\/BigComp48618.2020.00-89"},{"key":"4642_CR27","doi-asserted-by":"crossref","unstructured":"Kadry S, Rajinikanth V, Taniar D, Dama\u0161evi\u010dius R, Valencia XPB (2021) Automated segmentation of leukocyte from hematological images-a study using various cnn schemes. J Supercomput, pp 1\u201321","DOI":"10.1007\/s11227-021-04125-4"},{"key":"4642_CR28","unstructured":"Kosaraju A, Goyal A, Grigorova Y, Makaryus AN (2020) Left ventricular ejection fraction.[updated 2020 may 5]. StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing"},{"key":"4642_CR29","doi-asserted-by":"publisher","first-page":"16336","DOI":"10.1109\/ACCESS.2018.2816340","volume":"6","author":"D Krishnaswamy","year":"2018","unstructured":"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\u201316344","journal-title":"IEEE Access"},{"key":"4642_CR30","doi-asserted-by":"publisher","first-page":"195327","DOI":"10.1109\/ACCESS.2020.3034230","volume":"8","author":"Y Lan","year":"2020","unstructured":"Lan Y, Zhang X (2020) Real-time ultrasound image despeckling using mixed-attention mechanism based residual unet. IEEE Access 8:195327\u2013195340","journal-title":"IEEE Access"},{"issue":"8","key":"4642_CR31","doi-asserted-by":"publisher","first-page":"1651","DOI":"10.1109\/TMI.2012.2201737","volume":"31","author":"J Lebenberg","year":"2012","unstructured":"Lebenberg J, Buvat I, Lalande A, Clarysse P, Casta C, Cochet A, Constantinid\u00e9s 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\u20131660","journal-title":"IEEE Trans Med Imag"},{"key":"4642_CR32","doi-asserted-by":"crossref","unstructured":"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\u20135. IEEE","DOI":"10.1109\/NSSMIC.2013.6829398"},{"issue":"2","key":"4642_CR33","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1109\/TMI.2017.2743464","volume":"37","author":"O Oktay","year":"2017","unstructured":"Oktay O, Ferrante E, Kamnitsas K, Heinrich M, Bai W, Caballero J, Cook SA, De Marvao A, Dawes T, O\u2019Regan DP et al (2017) Anatomically constrained neural networks (acnns): application to cardiac image enhancement and segmentation. IEEE Trans Med Imag 37(2):384\u2013395","journal-title":"IEEE Trans Med Imag"},{"issue":"4","key":"4642_CR34","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1161\/01.CIR.43.4.480","volume":"43","author":"JF Pombo","year":"1971","unstructured":"Pombo JF, Troy BL, Russell ROJR (1971) Left ventricular volumes and ejection fraction by echocardiography. Circulation 43(4):480\u2013490","journal-title":"Circulation"},{"key":"4642_CR35","doi-asserted-by":"crossref","unstructured":"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\u2013252. IEEE","DOI":"10.1109\/IC3.2015.7346687"},{"key":"4642_CR36","doi-asserted-by":"crossref","unstructured":"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\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"3","key":"4642_CR37","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1023\/A:1007975324482","volume":"24","author":"WJ Rucklidge","year":"1997","unstructured":"Rucklidge WJ (1997) Efficiently locating objects using the hausdorff distance. Int J Comput Vis 24(3):251\u2013270","journal-title":"Int J Comput Vis"},{"issue":"23","key":"4642_CR38","doi-asserted-by":"publisher","first-page":"16770","DOI":"10.1109\/JIOT.2020.3029957","volume":"8","author":"Y Shen","year":"2021","unstructured":"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\u201316778","journal-title":"IEEE Internet Things J"},{"key":"4642_CR39","doi-asserted-by":"crossref","unstructured":"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\u2013104","DOI":"10.1109\/CCBD.2016.029"},{"key":"4642_CR40","doi-asserted-by":"crossref","unstructured":"Smistad E, \u00d8stvik A et\u00a0al. (2017) 2d left ventricle segmentation using deep learning. In: 2017 IEEE International Ultrasonics Symposium (IUS), pp 1\u20134. IEEE","DOI":"10.1109\/ULTSYM.2017.8092812"},{"issue":"12","key":"4642_CR41","doi-asserted-by":"publisher","first-page":"2595","DOI":"10.1109\/TUFFC.2020.2981037","volume":"67","author":"E Smistad","year":"2020","unstructured":"Smistad E, \u00d8stvik 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\u20132604","journal-title":"IEEE Trans Ultrasonics Ferroelectr Freq Control"},{"key":"4642_CR42","doi-asserted-by":"crossref","unstructured":"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\u2013410","DOI":"10.1109\/ICFHR.2016.0082"},{"key":"4642_CR43","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1016\/j.cmpb.2021.106268"},{"key":"4642_CR44","unstructured":"Zhang Y, Mehta S, Caspi A (2021) Rethinking semantic segmentation evaluation for explainability and model selection. arXiv:2101.08418"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-022-04642-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-022-04642-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-022-04642-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,28]],"date-time":"2024-09-28T09:36:13Z","timestamp":1727516173000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-022-04642-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,2]]},"references-count":44,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["4642"],"URL":"https:\/\/doi.org\/10.1007\/s11227-022-04642-w","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,2]]},"assertion":[{"value":"1 June 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 July 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}