Sökning: "segmentering patienter"

Visar resultat 1 - 5 av 10 uppsatser innehållade orden segmentering patienter.

  1. 1. Segmentation of x-ray images using deep learning trained on synthetic data

    Master-uppsats, KTH/Fysik

    Författare :Marcus Larsson; [2023]
    Nyckelord :Radiography; Deep Learning; Synthetic Data; Segmentation; Röntgen; Djupinlärning; Syntetisk Data; Segmentering;

    Sammanfattning : Radiograph examinations play a critical role in various applications such as the detection of bone pathologies and lung cancer, despite the challenge of false negatives. The integration of Artificial Intelligence (AI) holds promise in enhancing image quality and assisting radiologists in their diagnostic processes. LÄS MER

  3. 2. TransRUnet: 2D Detection and Segmentation of Lymphoma Lesions in Full-Body PET-CT Images

    Master-uppsats, KTH/Medicinteknik och hälsosystem

    Författare :Lasse Stahnke; [2023]
    Nyckelord :Lymphoma; PET-CT; Deep Learning; CNN; Retina U-Net; Feature Pyramid Transformer; Detection; Segmentation; Lymfom; PET-CT; djupinlärning; CNN; Retina U-Net; Feature Pyramid Transformer; detektion; segmentering;

    Sammanfattning : Identification and localization of FDG-avid lymphoma lesions in PET-CT image volumes is of high importance for the diagnosis and monitoring of treatment progress in lymphoma patients. This process is tedious, time-consuming, and error-prone, due to large image volumes and the heterogeneity of lesions. LÄS MER

  4. 3. Quantification of motor behaviour in freely moving rodents

    Master-uppsats, Lunds universitet/Avdelningen för Biomedicinsk teknik

    Författare :Caroline Kattrup Frostemark; [2022]
    Nyckelord :Technology and Engineering;

    Sammanfattning : Systems evaluating potential treatment methods for Parkinson’s disease and chronic pain conditions, using rodent experimental models, are highly needed. Currently used systems are aiming to evaluate these kinds of treatment methods by analyzing the animal’s motor behavior. LÄS MER

  5. 4. Deep Learning with Importance Sampling for Brain Tumor MR Segmentation

    Master-uppsats, KTH/Optimeringslära och systemteori

    Författare :Hanna Westermark; [2021]
    Nyckelord :Deep learning; importance sampling; segmentation; convolutional neural networks; MRI; brain tumour; Djupinlärning; importance sampling; segmentering; faltningsnätverk; MRI; hjärntumör;

    Sammanfattning : Segmentation of magnetic resonance images is an important part of planning radiotherapy treat-ments for patients with brain tumours but due to the number of images contained within a scan and the level of detail required, manual segmentation is a time consuming task. Convolutional neural networks have been proposed as tools for automated segmentation and shown promising results. LÄS MER

  7. 5. Data Augmentation to Improve Cross-Domain Generalization in Deep Learning MRI Segmentation

    Master-uppsats, Lunds universitet/Matematik LTH

    Författare :Rasmus Helander; [2021]
    Nyckelord :deep learning; medical imaging; mri; segmentation; data augmentation; cyclegan; noisy labels; Mathematics and Statistics;

    Sammanfattning : Semantic segmentation of medical images is an important task with many applications. However, manually delineating 3D images is time-consuming and the demand for automation is high. For many image segmentation tasks, deep learning has provided state-of-the-art results. LÄS MER