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master's thesis
Medical image segmentation using MedSAM model
Split: University of Split, Faculty of Science, 2024. urn:nbn:hr:166:740621
Rendulić, Marijana
University of Split
Faculty of Science
Department of Informatics

Institutional repository: Repository of Faculty of Science

Cite this document

Rendulić, M. (2024). Medical image segmentation using MedSAM model (Master's thesis). Split: University of Split, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:166:740621

Rendulić, Marijana. "Medical image segmentation using MedSAM model." Master's thesis, University of Split, Faculty of Science, 2024. https://urn.nsk.hr/urn:nbn:hr:166:740621

Rendulić, Marijana. "Medical image segmentation using MedSAM model." Master's thesis, University of Split, Faculty of Science, 2024. https://urn.nsk.hr/urn:nbn:hr:166:740621

Rendulić, M. (2024). 'Medical image segmentation using MedSAM model', Master's thesis, University of Split, Faculty of Science, accessed 24 November 2024, https://urn.nsk.hr/urn:nbn:hr:166:740621

Rendulić M. Medical image segmentation using MedSAM model [Master's thesis]. Split: University of Split, Faculty of Science; 2024 [cited 2024 November 24] Available at: https://urn.nsk.hr/urn:nbn:hr:166:740621

M. Rendulić, "Medical image segmentation using MedSAM model", Master's thesis, University of Split, Faculty of Science, Split, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:166:740621

Metadata
TitleMedical image segmentation using MedSAM model
Title (croatian)Segmentacija medicinskih slika upotrebom MedSAM modela
AuthorMarijana Rendulić
MentorSaša Mladenović (mentor)
Committee memberSaša Mladenović (predsjednik povjerenstva)
Committee memberGoran Zaharija (član povjerenstva)
Committee memberAntonela Prnjak (član povjerenstva)
GranterUniversity of Split
Faculty of Science
(Department of Informatics)
Split
Defense date and country2024-09-20, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
The use of artificial intelligence is becoming increasingly present in various spheres of modern society, including the field of medicine. This thesis is based on the newest technologies in the field of segmentation (MedSAM) and detection (RCS-YOLO). The algorithms are implemented in a demo application that aims to speed up the process of analyzing large medical image datasets to timely determine a potential suspicious region of the patient's brain, and at the same time to start timely treatment. The paper describes the limitations and further work with the purpose of improving the existing capabilities of the demo application. Also, a detailed methodology related to the dataset, chosen algorithms, and results is given.
Abstract (croatian)
Upotreba tehnologija umjetne inteligencije postaje sve prisutnija u različitim sferama modernog društva, pa tako i u području medicine. Ovaj rad temelji se na najnovijim tehnologijama u području segmentacije (MedSAM) i detekcije (RCS-YOLO) slike. Navedeni algoritmi implementirani su u demo aplikaciju koja ima za cilj ubrzavanje procesa analize velikih skupova medicinskih slika kako bi se pacijentu pravovremeno otkrila potencijalna sumnjiva regija na području mozga, a samim time i kako bi se pravovremeno krenulo sa tretmanom liječenja. U radu su opisana ograničenja i daljnji rad sa svrhom unapređenja i poopćenja postojećih mogućnosti demo aplikacije. Dana je i detaljna metodologija vezana uz skup podataka, odabrane algoritme i rezultate.
Keywords
Keywords (croatian)
Languageenglish
URN:NBNurn:nbn:hr:166:740621
Study programmeTitle: Data Science and Engineering
Study programme type: university
Study level: graduate
Academic / professional title: sveučilišni/a magistar/magistra podatkovne znanosti i inženjerstva (sveučilišni/a magistar/magistra podatkovne znanosti i inženjerstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-09-23 13:11:03