prikaz prve stranice dokumenta Duboko učenje za segmentaciju srca
No public access
master's thesis
Duboko učenje za segmentaciju srca
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. urn:nbn:hr:168:540585
Meštrović, Marko
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Meštrović, M. (2020). Duboko učenje za segmentaciju srca (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:540585

Meštrović, Marko. "Duboko učenje za segmentaciju srca." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:540585

Meštrović, Marko. "Duboko učenje za segmentaciju srca." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:540585

Meštrović, M. (2020). 'Duboko učenje za segmentaciju srca', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 20 May 2024, https://urn.nsk.hr/urn:nbn:hr:168:540585

Meštrović M. Duboko učenje za segmentaciju srca [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2020 [cited 2024 May 20] Available at: https://urn.nsk.hr/urn:nbn:hr:168:540585

M. Meštrović, "Duboko učenje za segmentaciju srca", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:168:540585

Metadata
TitleDuboko učenje za segmentaciju srca
Title (english)Deep Learning for Whole Heart Segmentation
AuthorMarko Meštrović
MentorSven Lončarić (mentor)
Committee memberSven Lončarić (predsjednik povjerenstva)
Committee memberMarko Subašić (član povjerenstva)
Committee memberTomislav Petković (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2020-07-08, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U ovome radu uspoređivane su dvije različite arhitekture neuronskih mreža za segmentaciju biomedicinskih slika. Slike iz korištenog skupa podataka su dobivene pomoću X-PCI metode snimanja srca štakora. Arhitekture mreža koje se koriste su U-net i Tiramisu. Segmentacija slike je proces dijeljenja digitalne slike na više smislenih, logično izabranih skupova piksela ili segmenata. Obje korištene mreže su konvolucijske mreže. Prema dobivenim rezultatima zaključuje se da točnost segmentacije U-net mreže manje ovisi o dimenzijama slika od Tiramisu. Tiramisu mreža ima puno dulje trajanje treniranja, te na nju puno više utječu dimenzije slika. Implementirana rješenja tog problema su prepolavljanje dimenzija slike i dijeljenje slike na četiri slike jednakih dimenzija. Mreže su uspoređivane pomoću Dice i IoU. Najbolji dobiveni rezultati su od Tiramisu mreže trenirane na skupu podataka slika podijeljenih na četiri jednakih dimenzija (Dice = 0.9540, IoU = 0.9112).
Abstract (english)
This work compares the effectiveness of two different neural network architectures for biomedical image segmentation. Images from the data set used were obtained using the X-PCI method on the heart of a rat. The architectures used in this paper are U-net and Tiramisu. Image segmentation is the process of dividing a digital image into multiple meaningful, logically chosen sets of pixels or segments. Both networks used are convolutional networks. According to the obtained results, it is concluded that the accuracy of U-net network segmentation depends less on the image dimensions than Tiramisu. The Tiramisu network has a much longer training duration, and it is much more affected by the dimensions of the images. Implemented solutions to this problem are halving the image dimensions and dividing the image into four images of equal dimensions. The best results were obtained from Tiramisu networks trained on a set of images divided into four equal dimensions (Dice = 0.9540, IoU = 0.9112).
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:540585
Study programmeTitle: Information and Communication Technology
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka informacijske i komunikacijske tehnologije (magistar/magistra inženjer/inženjerka informacijske i komunikacijske tehnologije)
Type of resourceText
File originBorn digital
Access conditionsClosed access
Terms of useLicence In copyright icon
Public note
Created on2023-01-13 10:25:35