Sökning: "3D Medical image segmentation"

Visar resultat 1 - 5 av 24 uppsatser innehållade orden 3D Medical image segmentation.

  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. 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

  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. Preparations for photon external beam radiotherapy treatment planning of small animals

    Master-uppsats, Lunds universitet/Sjukhusfysikerutbildningen

    Författare :Joanie Diha Guei; [2022]
    Nyckelord :Medicine and Health Sciences;

    Sammanfattning : Introduction:The XenX is a small animal irradiation system that acquires high-resolution cone-beam tomography (CBCT) images of small animals and treats small animals with higher precision than medical linear accelerators. MuriSlice is a software for CBCT image reconstruction. LÄS MER

  7. 5. Characterization of discrepancies between manual and automatic segmentation to improve anatomical brain atlases

    Master-uppsats,

    Författare :Anna Sörensson; [2021-05-10]
    Nyckelord :Medical physics; Anatomical brain atlas; Image segmentation; Image registration;

    Sammanfattning : Purpose: To characterize discrepancies between expert manually segmented brain images from Hammers Atlas Database and automatically generated segmentations of the same images; to decide whether they can be attributed to flaws in the automatic segmentation or in the manual segmentation; and to determine general rules that enable these decisions. Theory: Image segmentation plays an important role in clinical neuroscience and experimental medicine for extraction of information from medical images, and it is a fundamental image processing step in medical image analysis. LÄS MER