Sökning: "3D CNN"

Visar resultat 1 - 5 av 50 uppsatser innehållade orden 3D CNN.

  1. 1. Instance segmentation using 2.5D data

    Master-uppsats, Linköpings universitet/Institutionen för systemteknik

    Författare :Jonathan Öhrling; [2023]
    Nyckelord :instance segmentation; multi-modality; segmentation; multi-modality fusion; CNN; RGBD; ToF; Mask R-CNN; RTMDet; MMDetection; COCO; NYUDepth;

    Sammanfattning : Multi-modality fusion is an area of research that has shown promising results in the domain of 2D and 3D object detection. However, multi-modality fusion methods have largely not been utilized in the domain of instance segmentation. LÄS MER

  3. 2. Deep Learning-Based Bone Segmentation of the Metatarsophalangeal Joint : Using an Automatic and an Interactive Approach

    Master-uppsats, KTH/Skolan för kemi, bioteknologi och hälsa (CBH)

    Författare :Hannah Krogh; [2023]
    Nyckelord :Deep Learning; CNN; U-Net; DeepEdit; Bone Segmentation; CT; MTP Joint;

    Sammanfattning : The first Metatarsophalangeal (MTP) joint is essential for foot biomechanics and weight-bearing activities. Osteoarthritis in this joint can lead to pain, discomfort, and limited mobility. In order to treat this, Episurf Medical is working to produce individualized implants based on 3D segmentations of the joint. LÄS MER

  4. 3. Evaluation of Computer Tomography based Cancer Diagnostics with the help of 3D Printed Phantoms and Deep Learning

    Kandidat-uppsats, KTH/Skolan för teknikvetenskap (SCI)

    Författare :Alex Back; Pontus Pandurevic; [2023]
    Nyckelord :Machine Learning; Deep Learning; Convolutional Neural Networks; Perception Loss; CNN; DL; ML; Computer Tomography; CT; Cancer Diagnostics; Evaluation of Image Reconstruction; Radiomics; Tumors; 3D Printed Phantoms; 3D Printing; PLA;

    Sammanfattning : Computed x-ray tomography is one of the most common medical imaging modalities andas such ways of improving the images are of high relevance. Applying deep learningmethods to denoise CT images has been of particular interest in recent years. 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. Automatic Detection of Common Signal Quality Issues in MRI Data using Deep Neural Networks

    Master-uppsats, Linköpings universitet/Institutionen för medicinsk teknik

    Författare :Erika Ax; Elin Djerf; [2023]
    Nyckelord :mr; magnetic resonance; machine learning; deep learning; anomaly detection; U-Net; autoencoder; 3D; classification; reconstruction; artefacts;

    Sammanfattning : Magnetic resonance imaging (MRI) is a commonly used non-invasive imaging technique that provides high resolution images of soft tissue. One problem with MRI is that it is sensitive to signal quality issues. The issues can arise for various reasons, for example by metal located either inside or outside of the body. LÄS MER