Sökning: "DICE"
Visar resultat 11 - 15 av 133 uppsatser innehållade ordet DICE.
11. Deep Learning Based Detection, Quantification, and Subdivision of White Matter Hyperintensities in Brain MRI
Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen Vi3Sammanfattning : 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
12. Segmentation of x-ray images using deep learning trained on synthetic data
Master-uppsats, KTH/FysikSammanfattning : 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
13. Large-scale transfer of lesion segmentations from PET to CT
Master-uppsats, Uppsala universitet/Institutionen för informationsteknologiSammanfattning : The introduction of AI technology has sparked a revolutionary lesion segmentation solution to address challenges in the medical field. After extensive research, the competencies of this technology have been verified. LÄS MER
14. Performance Evaluation of Lumen Segmentation in Ultrasound Images
Kandidat-uppsats, Umeå universitet/Institutionen för datavetenskapSammanfattning : Automatic segmentation of the lumen of carotid arteries in ultrasound images is a starting step in providing preventive care for patients with atherosclerosis. To perform the segmentation this paper introduces a model utilizing a threshold algorithm. LÄS MER
15. Computer-Aided Characterization of Lung - Segmentation and Vessel Tree Analysis Algorithms for Clinical Research Applications
Master-uppsats, KTH/FysikSammanfattning : The initial stage of a lung examination involves the segmentation of a CT image, a process that has been put under a lot of pressure with the high demand for chest scans and accurate segmentations. Current automatic segmentation algorithms are either non-robust for different datasets, not easily accessible, or time-consuming. LÄS MER