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undergraduate thesis
Semantička segmentacija kralježnice na temelju snimaka magnetske rezonance
Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2023. urn:nbn:hr:235:534844
Radešić, Rikardo
University of Zagreb
Faculty of Mechanical Engineering and Naval Architecture

Institutional repository: Repository of Faculty of Mechanical Engineering and Naval Architecture University of Zagreb

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Radešić, R. (2023). Semantička segmentacija kralježnice na temelju snimaka magnetske rezonance (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture. Retrieved from https://urn.nsk.hr/urn:nbn:hr:235:534844

Radešić, Rikardo. "Semantička segmentacija kralježnice na temelju snimaka magnetske rezonance." Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2023. https://urn.nsk.hr/urn:nbn:hr:235:534844

Radešić, Rikardo. "Semantička segmentacija kralježnice na temelju snimaka magnetske rezonance." Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2023. https://urn.nsk.hr/urn:nbn:hr:235:534844

Radešić, R. (2023). 'Semantička segmentacija kralježnice na temelju snimaka magnetske rezonance', Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, accessed 23 November 2024, https://urn.nsk.hr/urn:nbn:hr:235:534844

Radešić R. Semantička segmentacija kralježnice na temelju snimaka magnetske rezonance [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture; 2023 [cited 2024 November 23] Available at: https://urn.nsk.hr/urn:nbn:hr:235:534844

R. Radešić, "Semantička segmentacija kralježnice na temelju snimaka magnetske rezonance", Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:235:534844

Metadata
TitleSemantička segmentacija kralježnice na temelju snimaka magnetske rezonance
Title (english)Semantic segmentation of the spine based on magnetic resonance imaging
AuthorRikardo Radešić
MentorMarko Švaco (mentor)
MentorFilip Šuligoj (komentor)
GranterUniversity of Zagreb
Faculty of Mechanical Engineering and Naval Architecture
Zagreb
Defense date and country2023-09-28, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Mechanical Engineering
Abstract
U sklopu ovog rada obrađena je tema računalnog vida za medicinsku svrhu. U medicini se koriste različite metode za segmentiranje dijelova tijela sa snimaka (npr. organi, kosti, razna tkiva..) i ovaj rad fokusira se na segmentaciju kralježnice snimaka magnetske rezonance pomoću algoritama strojnog vida. To znači da je program sam sposoban prepoznati svaki kralježak zasebno, te ih označiti. Ovim putem se cijeli postupak tumačenja sadržaja MRI snimaka može znatno olakšati i ubrzati. Postoje metode koje imaju tradicionalan pristup segmentacije, te one koje rade pomoću dubokih neuronskih mreža. Istraženo je više metoda u oba pristupa segmentaciji, kao što su CNN, FCN, U-Net, GAN, Edge detection, thresholding,.. Kasnije u radu segmentirane su dvije različite kralježnice otprilike istim pristupom, obje pomoću algoritama obrade slike. Dobiveni rezultati jedne od tih dviju segmentacija, uspoređene su pomoću Dice koeficijenta sličnosti sa State-of-the-art rješenjima segmentacije. Evaluirana je uspješnost segmentacije, te su doneseni zaključci. Cilj ovog rada je istražiti koliko se dobro može provesti postupak segmentacije pristupom obrade slike u usporedbi s dobro razvijenim već postojećim metodama segmentacije u medicini.
Abstract (english)
This paper focuses on the topic of computer vision for medical purposes. In medicine, various methods are employed for the segmentation of body parts from images, such as organs, bones, and various tissues. This paper specifically concentrates on segmenting the spine from magnetic resonance imaging (MRI) scans using machine vision algorithms. This means that the program is capable of autonomously recognizing and labeling each vertebra individually. This approach significantly streamlines and accelerates the process of interpreting the content of MRI scans. There are methods that follow a traditional segmentation approach, as well as those that utilize deep neural networks. Several methods have been explored in both segmentation approaches, including CNN, FCN, U-Net, GAN, edge detection, thresholding, and others. Later in the paper, two different spines were segmented using roughly the same image processing algorithms. The obtained results of one of these two segmentations were compared using the Dice coefficient of similarity with state-of-the-art segmentation solutions. The success of the segmentation was evaluated, and conclusions were drawn. The aim of this paper is to investigate how well the segmentation process can be conducted using image processing approaches compared to well-established existing methods of segmentation in medicine.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:235:534844
Study programmeTitle: Mechanical Engineering
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka strojarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka strojarstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2023-09-26 17:11:01