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master's thesis
Duboko učenje za segmentaciju tumora mozga iz MRI slika
Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2023. urn:nbn:hr:200:268639
Raguž, Maroje
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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Raguž, M. (2023). Duboko učenje za segmentaciju tumora mozga iz MRI slika (Master's thesis). Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek. Retrieved from https://urn.nsk.hr/urn:nbn:hr:200:268639

Raguž, Maroje. "Duboko učenje za segmentaciju tumora mozga iz MRI slika." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2023. https://urn.nsk.hr/urn:nbn:hr:200:268639

Raguž, Maroje. "Duboko učenje za segmentaciju tumora mozga iz MRI slika." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2023. https://urn.nsk.hr/urn:nbn:hr:200:268639

Raguž, M. (2023). 'Duboko učenje za segmentaciju tumora mozga iz MRI slika', Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, accessed 01 April 2025, https://urn.nsk.hr/urn:nbn:hr:200:268639

Raguž M. Duboko učenje za segmentaciju tumora mozga iz MRI slika [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; 2023 [cited 2025 April 01] Available at: https://urn.nsk.hr/urn:nbn:hr:200:268639

M. Raguž, "Duboko učenje za segmentaciju tumora mozga iz MRI slika", Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:200:268639

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Metadata
TitleDuboko učenje za segmentaciju tumora mozga iz MRI slika
Title (english)Deep learning for brain tumor segmentation from MRI images
AuthorMaroje Raguž
MentorIrena Galić (mentor)
Committee memberIrena Galić (predsjednik povjerenstva)
Committee memberHrvoje Leventić (član povjerenstva)
Committee memberKrešimir Romić (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 country2023-10-06, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Artificial Intelligence
Abstract
Tumori mozga, kao i svi ostali tumori nastaju nekontroliranom diobom stanica. Kako bih se ispravno planirala radioterapija, pratila progresija tumora te izabrala metoda liječenja, važno je ispravno ocrtavanje tumora. U ovom radu napravljen je model za automatsku segmentaciju GD pojačavajućeg tumora, peritumoralnog edema i nekrotične jezgre tumora bez povećanja. Za rješavanje ovog problema korištene su konvolucijske neuronske mreže, točnije U-Net arhitektura. Kod konvolucijskih neuronskih mreža... More protok informacija odvija se u samo jednom smjeru. U-Net arhitektura sastoji se od kodera, dekodera te mosta. Za izradu modela korišteni su Python, Keras, Google Colaboratory i ITK-snap. Za treniranje se koristio BraTS2020 skup podataka. Nakon predobrade i treniranja podataka dobiveni su sljedeći rezultati: Jaccard index iznosi 0.7123, Dice coefficient iznosi 0.7956, a iznos gubitka je 0.2151. Rezultati su relativno dobri, model se ne može koristiti kao zamjena za ručnu segmentaciju, ali se mogu koristiti za pomoć pri istoj. Less
Abstract (english)
Brain tumors, like all other tumors, arise from uncontrolled cell division. In order to properly plan radiotherapy, monitor tumor progression and choose a treatment method, it is important to correctly label the tumor. In this paper, a model for automatic segmentation of GD-enhancing tumor, peritumoral edema and necrotic non-enhancing tumor core. To solve this problem, convolutional neural networks, more precisely the U-Net architecture, were used. In convolutional neural networks,... More information flows in only one direction. The U-Net architecture consists of an encoder, a decoder and a bridge. Python, Keras, Google Collaboratory and ITK-snap were used to create the model. The BraTS2020 dataset was used for training. After data preprocessing and training, the following results were obtained: Jaccard index is 0.7123, Dice coefficient is 0.7956, and the amount of loss is 0.2151. The results are relatively good, the model cannot be used instead of manual segmentation, but it can be used to help with it. Brain tumors, like all other tumors, arise from uncontrolled cell division. In order to properly plan radiotherapy, monitor tumor progression and choose a treatment method, it is important to correctly label the tumor. In this paper, a model for automatic segmentation of GD-enhancing tumor, peritumoral edema and necrotic non-enhancing tumor core. To solve this problem, convolutional neural networks, more precisely the U-Net architecture, were used. In convolutional neural networks, information flows in only one direction. The U-Net architecture consists of an encoder, a decoder and a bridge. Python, Keras, Google Collaboratory and ITK-snap were used to create the model. The BraTS2020 dataset was used for training. After data preprocessing and training, the following results were obtained: Jaccard index is 0.7123, Dice coefficient is 0.7956, and the amount of loss is 0.2151. The results are relatively good, the model cannot be used instead of manual segmentation, but it can be used to help with it. Less
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:200:268639
Study programmeTitle: Graduate University Study Programme in Computer Engineering
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
RepositoryFaculty of Electrical Engineering, Computer Science and Information Technology Osijek
Created on2023-10-18 09:05:43
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