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master's thesis
Segmentacija lijevog atrija iz LGE MRI pomoću konvolucijske neuronske mreže
2020. urn:nbn:hr:200:249084
Topić, Tamara
Josip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
Department of Software Engineering
Chair of Visual Computing

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Topić, T. (2020). Segmentacija lijevog atrija iz LGE MRI pomoću konvolucijske neuronske mreže (Master's thesis). Retrieved from https://urn.nsk.hr/urn:nbn:hr:200:249084

Topić, Tamara. "Segmentacija lijevog atrija iz LGE MRI pomoću konvolucijske neuronske mreže." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2020. https://urn.nsk.hr/urn:nbn:hr:200:249084

Topić, Tamara. "Segmentacija lijevog atrija iz LGE MRI pomoću konvolucijske neuronske mreže." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2020. https://urn.nsk.hr/urn:nbn:hr:200:249084

Topić, T. (2020). 'Segmentacija lijevog atrija iz LGE MRI pomoću konvolucijske neuronske mreže', Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, accessed 24 November 2022, https://urn.nsk.hr/urn:nbn:hr:200:249084

Topić T. Segmentacija lijevog atrija iz LGE MRI pomoću konvolucijske neuronske mreže [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; 2020 [cited 2022 November 24] Available at: https://urn.nsk.hr/urn:nbn:hr:200:249084

T. Topić, "Segmentacija lijevog atrija iz LGE MRI pomoću konvolucijske neuronske mreže", Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:200:249084

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Metadata
TitleSegmentacija lijevog atrija iz LGE MRI pomoću konvolucijske neuronske mreže
Title (english)Left atrial segmentation from LGE MRI using a convolutional neural network
AuthorTamara Topić
MentorIrena Galić (mentor)
Committee memberIrena Galić (predsjednik povjerenstva)
Committee memberMirko Köhler (član povjerenstva)
Committee memberHrvoje Leventić (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
(Department of Software Engineering)
(Chair of Visual Computing)
Osijek
Defense date and country2020-09-18, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Artificial Intelligence
Abstract
Segmentacija dijelova srca iz kasno postkontrastno snimljenih slika magnetne rezonance (LGE MRI) predstavlja važan dio svakodnevne medicinske prakse kako bi se pravilno identificirala fibroza miokarda te drugih bolesti srca i kardiovaskularnog sustava. Međutim, automatska segmentacija i dalje predstavlja veliki izazov zbog heterogene raspodjele intenziteta i nejasnih granica između dijelova srca. U ovom radu predstavljena je automatska metoda segmentacije lijevog atrija iz trodimenzionalnih (3D) LGE MRI slika temeljena na dubokom učenju. Segmentacija lijevog atrija izvedena je pomoću konvolucijske neuronske mreže korištenjem U – Net modela. U-Net model treniran je pomoću izrazito malog skupa podataka od 6 3D LGE MRI slika. Unatoč tome, uspješno je ostvaren dice koeficijent od 0.67. Prema tome, u radu je pokazano da originalna U-Net arhitektura može ostvariti uspješan rezultat za segmentaciju lijevog atrija iz LGE-MRI slika upotrebom malenog broja podataka za učenje mreže.
Abstract (english)
Segmentation of the heart chambers from late gadolinium enhancement magnetic resonance images (LGE MRI) is an important part of everyday medical practice. It allows proper identification of myocardial fibrosis and other various heart and cardiovascular system diseases and conditions. However, automatic segmentation remains a major challenge due to the heterogeneous distribution of intensities and blurred boundaries between parts of the heart. This paper presents an automatic left atrial segmentation method from three-dimensional (3D) LGE MRI images based on deep learning. Left atrial segmentation was performed using a convolutional neural network using the U - Net model. The U-Net model was trained using an extremely small data set of 6 3D LGE MRI images. Despite this, a dice coefficient of 0.67 was successfully achieved. Therefore, the paper shows that the original U-Net architecture can successfully segmentation the left atrium from LGE-MRI images using a small amount of data for network learning.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:200:249084
Study programmeTitle: Graduate University Study Programme in Electrical Engineering; specializations in: Communications and Informatics, Power Engineering
Course: Communications and Informatics
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka elektrotehnike (magistar/magistra inženjer/inženjerka elektrotehnike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
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Created on2020-11-20 09:13:09