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005			20260818090357.0
008			220914s2022 th uu m rtt 0| a1eng d
035			_a.b12391761
099		9	_aAIT Thesis no.TC-22-02
100	1		_aBallais, Lalaine Jean Aragon
245	1	0	_aDetection of myocardial infarction in 2D echocardiograms and cardiac magnetic resonance images using deep learning
260			_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2022
300			_a55 leaves : _bill.
490	1		_aThesis ; _vno. TC-22-02
500			_aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Telecommunications
502			_aThesis (M. Eng.) - Asian Institute of Technology, 2022
520			_aIschemic heart disease (IHD) is among the world's principal cause of death and disabil- ity. In the Philippines alone, it is claimed that approximately 99.7 thousand Filipinos died in the year 2020 due to IHD and outranked the number of deaths due to any other disease, including Co ViD-19. IHD, when prolonged could result to the death of heart muscle cells, a condition known as myocardial infarction (MI). Many people expe- rience IHD without symptoms leaving it untreated and suddenly suffering from my- ocardial infarction. According to the global epidemiology for IHD, one of the clinical manifestations of IHD is myocardial infarction. Therefore, finding a way to diagnose myocardial infarction early is of great importance and is the subject of research for many scholars. Myocardial infarction can be diagnosed through imaging techniques such as the echocardiograms and cardiac magnetic resonance images (MRI). Analyz- ing echocardiograms and MRIs are manually done by radiologists and therefore time- consuming and an experience-dependent task. In both imaging modalities, quantifica- tion techniques using statistical signal processing and deep learning algorithms can be used in the detection of myocardial infarction. Quantification techniques, however, re- main a challenge due to the variability in image quality as well as variability in cardiac structures across different subjects. In this study, myocardial infarction is detected by quantifying left ventricle wall motion and myocardial thickening in echocardiograms and MRIs by applying statistical signal processing in a combination of attention U-Net and modified siamese neural network based deep learning architecture. In both imag- ing modalities, the measurement methods were compared. Then, the technique was compared to existing ones. Results show better performance on segmentation and MI detection on echocardiogram than on MRI.
650		0	_aDeep learning
650		0	_aEchocardiography, Two-Dimensional
650		0	_aCoronary heart disease _xDiagnosis imaging
700	0		_aAttaphongse Taparugssanagorn, _eChairperson
700	0		_aTeerapat Sanguankotchakorn, _eExamination Committee
700	0		_aChaklam Silpasuwanchai, _eExamination committee
710	2		_aAsian Development Bank - Japan Scholarship Program (ADB-JSP), _eScholarship Donor
810	2		_aAsian Institute of Technology. _tThesis ; _vno. TC-22-02
856			_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B17274
907			_a.b12391761 _bmnait _cm
902			_a240419
998			_b0 _c220914 _dm _eb _fm _g0
945			_lmnarc
945			_lmnarc
942			_c40
942			_c67
909			_aBarcode : 30020220005626 _bCREATED : 2022-09-13 _cRECORD # : i13399111 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0
909			_aBarcode : - _bCREATED : 2022-09-13 _cRECORD # : i13399123 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0
999			_c27892 _d27892