Sökning: "U-Net"

Visar resultat 6 - 10 av 103 uppsatser innehållade ordet U-Net.

  1. 6. Clinical Assessment of Deep Learning-Based Uncertainty Maps in Lung Cancer Segmentation

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

    Författare :Federica Carmen Maruccio; [2023]
    Nyckelord :3D U-Net; Contouring; Clinical validation; Deep learning; Lung cancer; Monte Carlo dropout; Probability map; Reliability diagram; Segmentation; Uncertainty map;

    Sammanfattning : Prior to radiation therapy planning, tumours and organs at risk need to be delineated. In recent years, deep learning models have opened the possibility of automating the contouring process, speeding up the procedures and helping clinicians. LÄS MER

  3. 7. 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. 8. Comparative Analysis of Transformer and CNN Based Models for 2D Brain Tumor Segmentation

    Master-uppsats, Linköpings universitet/Institutionen för medicinsk teknik

    Författare :Henrik Träff; [2023]
    Nyckelord :Machine Learning; ML; AI; Computer vision; Vision Transformer; Swin Transformer; U-Net; nnU-Net; Brain Tumor Segmentation; Deep Learning;

    Sammanfattning : A brain tumor is an abnormal growth of cells within the brain, which can be categorized into primary and secondary tumor types. The most common type of primary tumors in adults are gliomas, which can be further classified into high-grade gliomas (HGGs) and low-grade gliomas (LGGs). LÄS MER

  5. 9. Cell Identification from Microscopy Images using Deep Learning on Automatically Labeled Data

    Master-uppsats, Lunds universitet/Institutionen för elektro- och informationsteknik

    Författare :Fredrik Salomon-Sörensen; [2023]
    Nyckelord :Deep Learning; Cell Identification; Image Segmentation; Nuclei Segmentation; Convolutional Neural Networks; UNet; Image Analysis; Microscopy; Noisy Labels; Phase-Contrast; Automatic Labeling; Technology and Engineering;

    Sammanfattning : In biology, cell counting provides a fundamental metric for live-cell experiments. Unfortunately, most researchers are constrained to using tedious and invasive methods for counting cells. Automatic identification of cells in microscopy images would therefore be a valuable tool for such researchers. LÄS MER

  7. 10. 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