magistrsko delo
Aljaž Bavec (Author), Andrej Studen (Mentor), Andrej Doma (Co-mentor)

Abstract

Natančna in učinkovita segmentacija malignih limfomov na slikah pozitronske emisijske tomografije/računalniške tomografije s fluorodeoksiglukozo (FDG PET/CT) bi pripomogla pri pospešitvi in avtomatizaciji procesa segmentacije. Naloga raziskuje uporabo metode globokega učenja nnUNet pri segmentaciji slik limfoma. V nalogi so uporabljene slike FDG PET/CT 202 pacientov. Na slikah je bilo naučenih 9 različnih modelov z različnimi učnimi množicami in hitrostmi učenja. Z naučenimi modeli smo lahko zadovoljivo segmentirali območja limfomov, večja območja so modeli segmentirali najboljše medtem ko manjše lezije limfomov slabše. Izstopal je model, ki je imel največjo učno množico 143 pacientov in 10 h učenja, kar je bil drugi najdaljši čas učenja in je tudi primerljiv po vrednostih metrike Dice z ostalimi modeli segmentiranja tumorjev, ki jih najdemo v literaturi. Ugotovitve te raziskave prispevajo k potencialu umetne inteligence pri kliničnem delu ter ponazarjajo potencial metod globokega učenja, kot je nnUNet. Ker se področje analize medicinskih slik še naprej razvija, integracija naprednih tehnik umetne inteligence obeta bolj personalizirane in učinkovite zdravstvene rešitve.

Keywords

medicinska fizika;medicinsko slikanje;limfomi;pozitronska emisijska tomografija;računalniška tomografija;globoko učenje;

Data

Language: Slovenian
Year of publishing:
Typology: 2.09 - Master's Thesis
Organization: UL FMF - Faculty of Mathematics and Physics
Publisher: [A. Bavec]
UDC: 616-073
COBISS: 169893635 Link will open in a new window
Views: 29
Downloads: 6
Average score: 0 (0 votes)
Metadata: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Other data

Secondary language: English
Secondary title: Segmentation of lymphomas in PET/CT images with artificial intelligence
Secondary abstract: Accurate and efficient segmentation of malignant lymphomas on fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) images would help to speed up and automate the segmentation process. This thesis explores deep learning method nnUNet in the context of lymphoma image segmentation. FDG PET/CT images of 202 patients were used in this thesis. The images were used to train 9 different models with different training sets and the duration’s of learning. We were able to satisfactorily segment areas of lymphomas with trained models, where larger areas of lymphoma involvement were better segmented by the models while smaller lesions were associated with worse segmentation results. The model that stood out was the one with the largest training set of 143 patients and the duration of training of 10 h, which was the second longest training time, being also comparable in terms of Dice metric values to other tumour segmentation models found in the literature. The findings of this research highlight potential of AI in clinical work and illustrate the promising features of deep learning methods such as nnUNet. As the field of medical image analysis continues to evolve, the integration of advanced AI techniques promises more personalised and efficient healthcare solutions.
Secondary keywords: medical physics;medical imaging;lymphoma;positron emission tomography;computed tomography;deep learning;nnUNet;
Type (COBISS): Master's thesis/paper
Study programme: 0
Embargo end date (OpenAIRE): 1970-01-01
Thesis comment: Univ. v Ljubljani, Fak. za matematiko in fiziko, Oddelek za fiziko
Pages: 68 str.
ID: 20488329