Sökning: "Medicinsk bildsegmentering"

Visar resultat 1 - 5 av 11 uppsatser innehållade orden Medicinsk bildsegmentering.

1. 1. Self-learning for 3D segmentation of medical images from single and few-slice annotation

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Côme Lassarat; [2023]
   Nyckelord :Self-supervised Learning; Segmentation; Medical images; Självövervakad inlärning; segmentering; medicinska bilder;

   Sammanfattning : Training deep-learning networks to segment a particular region of interest (ROI) in 3D medical acquisitions (also called volumes) usually requires annotating a lot of data upstream because of the predominant fully supervised nature of the existing stateof-the-art models. To alleviate this annotation burden for medical experts and the associated cost, leveraging self-learning models, whose strength lies in their ability to be trained with unlabeled data, is a natural and straightforward approach. LÄS MER

3. 2. Optic nerve sheath diameter semantic segmentation and feature extraction

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Simone Bonato; [2023]
   Nyckelord :Machine Learning; Computer Vision; Image Segmentation; Medical Imaging; Optic Nerve Sheath Diameter; nnU-Net; Maskininlärning; datorseende; bildsegmentering; medicinsk bildbehandling; optisk nervslidsdiameter; nnU-Net;

   Sammanfattning : Traumatic brain injury (TBI) affects millions of people worldwide, leading to significant mortality and disability rates. Elevated intracranial pressure (ICP) resulting from TBI can cause severe complications and requires early detection to improve patient outcomes. LÄS MER

4. 3. Self-supervised pre-training of an attention-based model for 3D medical image segmentation

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Albert Sund Aillet; [2023]
   Nyckelord :Computer vision; Deep learning; 3D Medical image segmentation; Self-supervised learning; Datorseende; Djupinlärning; 3D Medicinsk bildsegmentering; Självövervakad träning;

   Sammanfattning : Accurate segmentation of anatomical structures is crucial for radiation therapy in cancer treatment. Deep learning methods have been demonstrated effective for segmentation of 3D medical images, establishing the current standard. However, they require large amounts of labelled data and suffer from reduced performance on domain shift. LÄS MER

5. 4. Uncertainty Estimation in Volumetric Image Segmentation

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Donggyun Park; [2023]
   Nyckelord :Uncertainty Estimation; Uncertainty Quantification UQ ; Volumetric Image Segmentation; 3D U-Net; test-time data augmentation; Deep ensemble;

   Sammanfattning : The performance of deep neural networks and estimations of their robustness has been rapidly developed. In contrast, despite the broad usage of deep convolutional neural networks (CNNs)[1] for medical image segmentation, research on their uncertainty estimations is being far less conducted. LÄS MER

7. 5. Segmentering av medicinska bilder med inspiration från en quantum walk algoritm

   Kandidat-uppsats, KTH/Medicinteknik och hälsosystem

   Författare :Bestun Altuni; Jasin Aman Ali; [2023]
   Nyckelord :Discrete time quantum walk; medical image segmentation; random walk; quantum walk; Tidsdiskret kvantvandring; medicinsk bildsegmentering; slumpmässig vandring; kvantvandring;

   Sammanfattning : För närvarande utforskas quantum walk som en potentiell metod för att analysera medicinska bilder. Med inspiration från Gradys random walk-algoritm för bildbehandling har vi utvecklat en metod som bygger på de kvantmekaniska fördelar som quantum walk innehar för att detektera och segmentera medicinska bilder. LÄS MER