Brain metastasis tumor segmentation and detection using deep learning algorithms: a systematic review and meta-analysis
Background Manual detection of brain metastases is both laborious and inconsistent, driving
the need for more efficient solutions. Accordingly, our systematic review and meta-analysis …
the need for more efficient solutions. Accordingly, our systematic review and meta-analysis …
Artificial intelligence enables whole-body positron emission tomography scans with minimal radiation exposure
Purpose To generate diagnostic 18 F-FDG PET images of pediatric cancer patients from
ultra-low-dose 18 F-FDG PET input images, using a novel artificial intelligence (AI) …
ultra-low-dose 18 F-FDG PET input images, using a novel artificial intelligence (AI) …
[HTML][HTML] Artificial intelligence detection and segmentation models: a systematic review and meta-analysis of brain tumors in magnetic resonance imaging
Objective To thoroughly analyze factors affecting the generalization ability of deep learning
algorithms on brain tumor detection and segmentation models. Patients and Methods We …
algorithms on brain tumor detection and segmentation models. Patients and Methods We …
Handling missing MRI sequences in deep learning segmentation of brain metastases: a multicenter study
The purpose of this study was to assess the clinical value of a deep learning (DL) model for
automatic detection and segmentation of brain metastases, in which a neural network is …
automatic detection and segmentation of brain metastases, in which a neural network is …
2.5 D and 3D segmentation of brain metastases with deep learning on multinational MRI data
Introduction Management of patients with brain metastases is often based on manual lesion
detection and segmentation by an expert reader. This is a time-and labor-intensive process …
detection and segmentation by an expert reader. This is a time-and labor-intensive process …
Stratified assessment of an FDA-cleared deep learning algorithm for automated detection and contouring of metastatic brain tumors in stereotactic radiosurgery
JY Wang, V Qu, C Hui, N Sandhu, MG Mendoza… - Radiation …, 2023 - Springer
Purpose Artificial intelligence-based tools can be leveraged to improve detection and
segmentation of brain metastases for stereotactic radiosurgery (SRS). VBrain by Vysioneer …
segmentation of brain metastases for stereotactic radiosurgery (SRS). VBrain by Vysioneer …
Extended nnU-Net for brain metastasis detection and segmentation in contrast-enhanced magnetic resonance imaging with a large multi-institutional data set
Purpose The purpose of this study was to investigate an extended self-adapting nnU-Net
framework for detecting and segmenting brain metastases (BM) on magnetic resonance …
framework for detecting and segmenting brain metastases (BM) on magnetic resonance …
Evaluating deep learning methods in detecting and segmenting different sizes of brain metastases on 3D post-contrast T1-weighted images
Purpose: We investigate the impact of various deep-learning-based methods for detecting
and segmenting metastases with different lesion volume sizes on 3D brain MR images …
and segmenting metastases with different lesion volume sizes on 3D brain MR images …
Correlating volumetric and linear measurements of brain metastases on MRI scans using intelligent automation software: a preliminary study
Abstract Purpose The Response Assessment in Neuro-Oncology Brain Metastases (RANO-
BM) working group proposed a guide for treatment responses for BMs by utilizing the …
BM) working group proposed a guide for treatment responses for BMs by utilizing the …
[PDF][PDF] 深度学习重建在改善磁共振神经黑色素图像质量中的价值研究
于阳， 赵澄， 齐志刚， 吴涛， 卢洁 - 磁共振成像, 2023 - med-sci.cn
目的探讨深度学习重建(deep learning reconstruction, DL Recon) 在改善神经黑色素MRI
序列图像质量中的价值. 材料与方法前瞻性纳入2022 年5 月10 日至2022 年5 月31 …
序列图像质量中的价值. 材料与方法前瞻性纳入2022 年5 月10 日至2022 年5 月31 …