Semantička segmentacija kralježnice na temelju snimaka magnetske rezonance

undergraduate thesis

• Title (english): Semantic segmentation of the spine based on magnetic resonance imaging
• Author: Rikardo Radešić
• Mentor: Marko Švaco (mentor)
• Mentor: Filip Šuligoj (komentor)
• Granter: University of Zagreb; Faculty of Mechanical Engineering and Naval Architecture; Zagreb
• Defense date and country: 2023-09-28, 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: strojni vid; duboko učenje; segmentacija; umjetna inteligencija; kralježnica; obrada slike; medicina
• Keywords (english): computer vision; deep learning; segmentation; artificial intelligence; spine; image processing; medicine
• Language: croatian
• URN:NBN: urn:nbn:hr:235:534844
• Study programme: Title: 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 resource: Text
• File origin: Born digital
• Access conditions: Open access
• Created on: 2023-09-26 17:11:01