comparemela.com

Latest Breaking News On - Image registration - Page 1 : comparemela.com

Viability of focal dose escalation to prostate cancer intraprostatic l by Joel Poder, Samantha Radvan et al

PURPOSE: This study aimed to determine the viability of focal dose escalation to prostate cancer intraprostatic lesions (IPLs) from multiparametric magnetic resonance (mpMRI) and prostate-specific membrane antigen positron emission tomography (PSMA-PET) images using high-dose-rate (HDR) prostate brachytherapy (pBT). METHODS AND MATERIALS: Retrospective data from 20 patients treated with HDR pBT was utilized. The interobserver contouring variability of 5 observers was quantified using the dice similarity coefficient (DSC) and mean distance to agreement (MDA). Uncertainty in propagating IPL contours to trans-rectal ultrasound (TRUS) was quantified using a tissue equivalent prostate phantom. Feasibility of incorporating IPLs into HDR pBT planning was tested on retrospective patient data. RESULTS: The average observer DSC was 0.65 (PSMA-PET) and 0.52 (mpMRI). The uncertainty in propagating IPL contours was 0.6 mm (PSMA-PET), and 0.4 mm (mpMRI). Uncertainties could be accounted for by expan

Repeatability of MRI for radiotherapy planning for pelvic, brain, and by Monique Y Heinke, Lois Holloway et al

Objectives: The objective of this study was to assess the repeatability of MRI for the purpose of radiotherapy treatment planning by considering the difference in registering MRI to MRI compared to registering CT and MRI. Methods: Fifty patients undergoing radiotherapy planning for gynaecological, prostate, rectal, head and neck and CNS malignancies had a planning CT followed by two T2-weighted MRIs. Anatomical landmarks were contoured on each dataset and the images were rigidly registered. Centre of Mass (COM), Dice Similarity Coefficient (DSC), and Mean Distance to Agreement (MDA) were calculated to assess structure volume and position comparing CT-MRI and MRI-MRI. Results: DSC and MDA demonstrated more consistency in delineated volumes for MRI-MRI than for the CT-MRI comparison. The median DSC values were ≥0.8 for 15 of 46 contoured structures for the CT-MRI comparison and 21 of 23 structures for the MRI-MRI comparison. MDA values were ≤1 mm for 11 of 46 structures for the CT-MR

UK students earn top rankings for scientific research presentations

Corista-Led Study Shows Potential to Improve Renal Biopsy Analysis

Corista-Led Study Shows Potential to Improve Renal Biopsy Analysis Share Article Journal of Pathology Informatics Editorial Calls Concept “Sound” and Results “Encouraging” Journal of Pathology Informatics Editorial calls Corista-led study on use of AI and machine learning for renal biopsy analysis “Sound” and Results “Encouraging” CONCORD, Mass. (PRWEB) June 09, 2021 A Corista-led study on the impact of applying artificial intelligence to the workflow of a Renal Pathologist and how this might ease and improve the Pathologist’s workflow in reviewing renal biopsy slides has received an editorial stamp of approval from the Journal of Pathology Informatics. “Although being a limited proof-of-concept study on a small number of cases, the concept appears sound, and the results were encouraging,” a commentary on “The Digital Fate of Glomeruli in Renal Biopsy” noted in the March 22 edition of

Unsupervised Image Registration for Video SAR by Xuejun Huang, Jinshan Ding et al

Existing approaches for SAR image registration focus on the global transformation correction between SAR images. However, there are often local deformations between images. Due to the time-changing viewpoint of video SAR, the images suffer a lot from local deformations, which can result in false alarms in moving target detection. This article presents an unsupervised image registration approach for the use of video SAR moving target detection, which has good registration performance and acceptable processing efficiency. The designed unsupervised learning-based framework is a cascade of two convolutional neural networks. The first network directly predicts the parameters of the rigid transformation between the reference and unregistered images, and recovers the global transformation between them. Then, the second network uses the reference image and the registered image from the first network as input and then predicts a displacement field. After that, we put a limitation on the predict

© 2025 Vimarsana

vimarsana © 2020. All Rights Reserved.