Left Ventricular Segmentation, Warping, and Myocardial Registration for Automated Strain Measurement

Journal of Imaging Informatics in Medicine
Kuo‐Feng Huang1, Donna Shu‐Han Lin2, Geng-Shi Jeng3, Ting‐Tse Lin4, Lian‐Yu Lin4, Chih‐Kuo Lee5, Lung-Chun Lin4
1Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
2Division of Cardiology, Department of Internal Medicine, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
3Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
4Section of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
5National Taiwan University Hospital Hsin-Chu Branch, HsinChu, Taiwan

Tóm tắt

AbstractThe left ventricular global longitudinal strain (LVGLS) is a crucial prognostic indicator. However, inconsistencies in measurements due to the speckle tracking algorithm and manual adjustments have hindered its standardization and democratization. To solve this issue, we proposed a fully automated strain measurement by artificial intelligence-assisted LV segmentation contours. The LV segmentation model was trained from echocardiograms of 368 adults (11,125 frames). We compared the registration-like effects of dynamic time warping (DTW) with speckle tracking on a synthetic echocardiographic dataset in experiment-1. In experiment-2, we enrolled 80 patients to compare the DTW method with commercially available software. In experiment-3, we combined the segmentation model and DTW method to create the artificial intelligence (AI)-DTW method, which was then tested on 40 patients with general LV morphology, 20 with dilated cardiomyopathy (DCMP), and 20 with transthyretin-associated cardiac amyloidosis (ATTR-CA), 20 with severe aortic stenosis (AS), and 20 with severe mitral regurgitation (MR). Experiments-1 and -2 revealed that the DTW method is consistent with dedicated software. In experiment-3, the AI-DTW strain method showed comparable results for general LV morphology (bias − 0.137 ± 0.398%), DCMP (− 0.397 ± 0.607%), ATTR-CA (0.095 ± 0.581%), AS (0.334 ± 0.358%), and MR (0.237 ± 0.490%). Moreover, the strain curves showed a high correlation in their characteristics, with R-squared values of 0.8879–0.9452 for those LV morphology in experiment-3. Measuring LVGLS through dynamic warping of segmentation contour is a feasible method compared to traditional tracking techniques. This approach has the potential to decrease the need for manual demarcation and make LVGLS measurements more efficient and user-friendly for daily practice.

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Tài liệu tham khảo

Marwick TH, Shah SJ, Thomas JD: Myocardial Strain in the Assessment of Patients With Heart Failure: A Review. JAMA Cardiol. 4:287-294, 2019.

Voigt JU, Pedrizzetti G, Lysyansky P, Marwick TH, Houle H, Baumann R, Pedri S, Ito Y, Abe Y, Metz S, Song JH, Hamilton J, Sengupta PP, Kolias TJ, d'Hooge J, Aurigemma GP, Thomas JD, Badano LP: Definitions for a common standard for 2D speckle tracking echocardiography: consensus document of the EACVI/ASE/Industry Task Force to standardize deformation imaging. Eur Heart J Cardiovasc Imaging. 16:1-11, 2015.

Amzulescu MS, De Craene M, Langet H, Pasquet A, Vancraeynest D, Pouleur AC, Vanoverschelde JL, Gerber BL: Myocardial strain imaging: review of general principles, validation, and sources of discrepancies. Eur Heart J Cardiovasc Imaging. 20:605-619, 2019.

Farsalinos KE, Daraban AM, Ünlü S, Thomas JD, Badano LP, Voigt JU: Head-to-Head Comparison of Global Longitudinal Strain Measurements among Nine Different Vendors: The EACVI/ASE Inter-Vendor Comparison Study. J Am Soc Echocardiogr. 28:1171-1181, 2015.

Ünlü S, Mirea O, Duchenne J, Pagourelias ED, Bézy S, Thomas JD, Badano LP, Voigt JU: Comparison of Feasibility, Accuracy, and Reproducibility of Layer-Specific Global Longitudinal Strain Measurements Among Five Different Vendors: A Report from the EACVI-ASE Strain Standardization Task Force. J Am Soc Echocardiogr. 31:374-380, 2018.

Aldaas OM, Igata S, Raisinghani A, Kraushaar M, DeMaria AN: Accuracy of left ventricular ejection fraction determined by automated analysis of handheld echocardiograms: A comparison of experienced and novice examiners. Echocardiography. 36:2145-2151, 2019.

Narang A, Bae R, Hong H, Thomas Y, Surette S, Cadieu C, Chaudhry A, Martin RP, McCarthy PM, Rubenson DS, Goldstein S, Little SH, Lang RM, Weissman NJ, Thomas JD: Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use. JAMA Cardiol. 6:624-632,2021.

Asch FM, Mor-Avi V, Rubenson D, Goldstein S, Saric M, Mikati I, Surette S, Chaudhry A, Poilvert N, Hong H, Horowitz R, Park D, Diaz-Gomez JL, Boesch B, Nikravan S, Liu RB, Philips C, Thomas JD, Martin RP, Lang RM: Deep Learning-Based Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction: A Point-of-Care Solution. Circ Cardiovasc Imaging. 6:e012293, 2021.

Carneiro G, Nascimento JC: Combining multiple dynamic models and deep learning architectures for tracking the left ventricle endocardium in ultrasound data. IEEE Trans Pattern Anal Mach Intell. 35:2592-607, 2013.

Leclerc S, Smistad E, Pedrosa J, Ostvik A, Cervenansky F, Espinosa F, Espeland T, Berg EAR, Jodoin PM, Grenier T, Lartizien C, Dhooge J, Lovstakken L, Bernard O: Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography. IEEE Trans Med Imaging. 38:2198-2210, 2019.

Amer A, Ye X, Zolgharni M, Janan F: ResDUnet: Residual Dilated UNet for Left Ventricle Segmentation from Echocardiographic Images. Annu Int Conf IEEE Eng Med Biol Soc. 2020:2019-2022, 2020.

Wu JF, Liu QH, Jiang Y, Bai S, Yuille A, Bai X: In Defense of On-line Models for Video Instance Segmentation. Available at: https://arxiv.org/abs/2207.10661v1. Accessed 21 July 2022.

Bellman R, Kalaba R: On adaptive control processes. IRE Transactions on Automatic Control. 4:1-9, 1959.

Itakura F: Minimum Prediction Residual Principle Applied to Speech Recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing. 23:67-72, 1975.

Gaspar M, Welke B, Seehaus F, Hurschler C, Schwarze M: Dynamic Time Warping compared to established methods for validation of musculoskeletal models. J Biomech. 55:156-161, 2017.

Alessandrini M, De Craene M, Bernard O, Giffard-Roisin S, Allain P, Waechter-Stehle I, Weese J, Saloux E, Delingette H, Sermesant M, D'hooge J: A Pipeline for the Generation of Realistic 3D Synthetic Echocardiographic Sequences: Methodology and Open-access Database. IEEE Trans Med Imaging. 34:1436-1451, 2015.

Alessandrini M, Chakraborty B, Heyde B, Bernard O, De Craene M, Sermesant M, D'Hooge J: Realistic Vendor-Specific Synthetic Ultrasound Data for Quality Assurance of 2D Speckle Tracking Echocardiography: Simulation Pipeline and Open Access Database. IEEE Trans Ultrason Ferroelectr Freq Control. 65:411-422, 2018.

He K, Gkioxari G, Dollar P, Girshick R: Mask R-CNN. IEEE Trans Pattern Anal Mach Intell. 42:386-397, 2020.

Kawakami H, Wright L, Nolan M, Potter EL, Yang H, Marwick TH: Feasibility, Reproducibility, and Clinical Implications of the Novel Fully Automated Assessment for Global Longitudinal Strain. J Am Soc Echocardiogr. 34:136-145, 2021.

Salte IM, Østvik A, Smistad E, Melichova D, Nguyen TM, Karlsen S, Brunvand H, Haugaa KH, Edvardsen T, Lovstakken L, Grenne B: Artificial Intelligence for Automatic Measurement of Left Ventricular Strain in Echocardiography. J Am Coll Cardiol Img. 14:1918-1928, 2021.

Ostvik A, Salte IM, Smistad E, Nguyen TM, Melichova D, Brunvand H, Haugaa K, Edvardsen T, Grenne B, Lovstakken L: Myocardial Function Imaging in Echocardiography Using Deep Learning. IEEE Trans Med Imaging. 40:1340-1351, 2021.

Salte IM, Østvik A, Olaisen SH, Karlsen S, Dahlslett T, Smistad E, Eriksen-Volnes TK, Brunvand H, Haugaa KH, Edvardsen T, Dalen H, Lovstakken L, Grenne B: Deep Learning for Improved Precision and Reproducibility of Left Ventricular Strain in Echocardiography: A Test-Retest Study. J Am Soc Echocardiogr: 36:788-99, 2023.

Sperry BW, Sato K, Phelan D, Grimm R, Desai MY, Hanna M, Jaber WA, Popović ZB: Regional Variability in Longitudinal Strain Across Vendors in Patients With Cardiomyopathy Due to Increased Left Ventricular Wall Thickness. Circ Cardiovasc Imaging. 12:e008973, 2019.

Painchaud N, Duchateau N, Bernard O, Jodoin PM: Echocardiography Segmentation with Enforced Temporal Consistency. IEEE Trans Med Imaging. 41:2867-2878, 2022.

Laumer F, Di Vece D, Cammann VL, Würdinger M, Petkova V, Schönberger M, Schönberger A, Mercier JC, Niederseer D, Seifert B, Schwyzer M, Burkholz R, Corinzia L, Becker AS, Scherff F, Brouwers S, Pazhenkottil AP, Dougoud S, Messerli M, Tanner FC, Fischer T, Delgado V, Schulze PC, Hauck C, Maier LS, Nguyen H, Surikow SY, Horowitz J, Liu K, Citro R, Bax J, Ruschitzka F, Ghadri JR, Buhmann JM, Templin C: Assessment of Artificial Intelligence in Echocardiography Diagnostics in Differentiating Takotsubo Syndrome From Myocardial Infarction. JAMA Cardiol. 7:494-503, 2022.

Sengupta PP, Huang YM, Bansal M, Ashrafi A, Fisher M, Shameer K, Gall W, Dudley JT: Cognitive Machine-Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis From Restrictive Cardiomyopathy. Circ Cardiovasc Imaging. 9:e004330, 2016.

Chiou YA, Hung CL, Lin SF. AI-Assisted Echocardiographic Prescreening of Heart Failure With Preserved Ejection Fraction on the Basis of Intrabeat Dynamics. J Am Coll Cardiol Img. 14:2091-2104, 2021.

Upton R, Mumith A, Beqiri A, Parker A, Hawkes W, Gao S, Porumb M, Sarwar R, Marques P, Markham D, Kenworthy J, O'Driscoll JM, Hassanali N, Groves K, Dockerill C, Woodward W, Alsharqi M, McCourt A, Wilkes EH, Heitner SB, Yadava M, Stojanovski D, Lamata P, Woodward G, Leeson P: Automated Echocardiographic Detection of Severe Coronary Artery Disease Using Artificial Intelligence. J Am Coll Cardiol Img. 15:715-727, 2022.

Russell K, Eriksen M, Aaberge L, Wilhelmsen N, Skulstad H, Remme EW, Haugaa KH, Opdahl A, Fjeld JG, Gjesdal O, Edvardsen T, Smiseth OA: A novel clinical method for quantification of regional left ventricular pressure-strain loop area: a non-invasive index of myocardial work. Eur Heart J. 33:724–733, 2012.

Olsen FJ, Skaarup KG, Lassen MCH, Johansen ND, Sengeløv M, Jensen GB, Schnohr P, Marott JL, Søgaard P, Gislason G, Svendsen JH, Møgelvang R, Aalen JM, Remme EW, Smiseth OA, Biering-Sørensen T: Normal Values for Myocardial Work Indices Derived From Pressure-Strain Loop Analyses: From the CCHS. Circ Cardiovasc Imaging. 15:e013712, 2022.

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Journal of Imaging Informatics in Medicine

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