Sökning: "Volumetric Image Segmentation"

Visar resultat 1 - 5 av 14 uppsatser innehållade orden Volumetric Image Segmentation.

  1. 1. Volumetric Image Segmentation of Lizard Brains

    Master-uppsats, KTH/Tillämpad fysik

    Författare :Yulia Dragunova; [2023]
    Nyckelord :image segmentation; deep learning; atlas; lizard brain; micro-CT; image augmentation; bild segmentering; djupinlärning; atlas; ödelhjärna; mikro-datortomagrofi; datautökning;

    Sammanfattning : Accurate measurement brain region volumes are important in studying brain plasticity, which brings insight into the fundamental mechanisms in animal, memory, cognitive, and behavior research. The traditional methods of brain volume measurements are ellipsoid or histology. LÄS MER

  3. 2. Deep Learning Based Detection, Quantification, and Subdivision of White Matter Hyperintensities in Brain MRI

    Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen Vi3

    Författare :Inna Fryckstedt; [2023]
    Nyckelord :white matter hyperintensities; white matter lesions; deep learning; machine learning; DL; ML; MRI; iNPH;

    Sammanfattning : White matter hyperintensities (WMH) are commonly found as bright regions in brain MRI images in older individuals. They are associated with various neurological and vascular diseases, such as stroke, dementia, and cardiovascular disorders. LÄS MER

  4. 3. 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

  5. 4. Establishment of a deep learning algorithm for dosimetry of radiopharmaceuticals

    Master-uppsats,

    Författare :Dennis Lipnicevic; [2022-01-13]
    Nyckelord :Medical physics; automated segmentation; dosimetry; kidney; nuclear medicine dosimetry; deep learning; convolutional neural network;

    Sammanfattning : Purpose: The aim of this study was to introduce a volumetric convolutional neural network for segmentation of the kidneys in SPECT images and to apply it in the dosimetry of radiopharmaceuticals of this organ, in order to decrease segmentation time and to standardize the segmentation of the kidneys. Method: Three networks were trained using two network architectures and a total of 216 retrospectively collected images from patients that underwent imaging procedures at Sahlgrenska University Hospital between 2009 and 2018. LÄS MER

  7. 5. Automatic segmentation of microstructures in metals : A computer vision approach

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

    Författare :Oscar Larsen; [2022]
    Nyckelord :Image Processing; Computer Vision; Machine Learning; Deep Learning; Metallurgy; Segmentation; Bildbehandling; Datorseende; Maskininlärning; Djupinlärning; Metallurgi; Segmentering;

    Sammanfattning : The microstructures of a metal determine the physical, electrical and chemical properties of the metal, and analysis of microstructures is therefore an important part of materials science. Modern microscopes are capable of generating a lot of high-quality micrographs in a short period of time. LÄS MER