Analyzing magnetic resonance imaging data from glioma patients using deep learning-Reference-Cited by-同舟云学术

Analyzing magnetic resonance imaging data from glioma patients using deep learning

Published:2021-03 Issue: Volume:88 Page:101828
ISSN:0895-6111
Container-title:Computerized Medical Imaging and Graphics
language:en
Short-container-title:Computerized Medical Imaging and Graphics

Author:

Menze Bjoern,Isensee Fabian,Wiest Roland,Wiestler Bene,Maier-Hein Klaus,Reyes Mauricio,Bakas Spyridon

Funder

National Institutes of Health

National Institute of Neurological Disorders and Stroke

Krebsliga Schweiz

National Cancer Institute

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference140 articles.

1. A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning;Agn;Med. Image Anal.,2019

2. Histopathology-validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo-progression in glioblastoma;Akbari;Cancer,2020

3. Predicting deletion of chromosomal arms 1p/19q in low-grade gliomas from MR images using machine intelligence;Akkus;J. Digit. Imaging,2017

4. Radiotherapy quality assurance for the RTOG 0834/EORTC 26053-22054/NCIC CTG CEC. 1/CATNON intergroup trial “concurrent and adjuvant temozolomide chemotherapy in newly diagnosed non-1p/19q deleted anaplastic glioma”: individual case review analysis;André;Radiother. Oncol.,2018

5. The university of pennsylvania glioblastoma imaging, clinical, molecular, and radiomics (UPenn-GBM-ICMR) collection;Bakas;Sci. Data,2020

Cited by 30 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Negligible effect of brain MRI data preprocessing for tumor segmentation;Biomedical Signal Processing and Control;2024-10

2. Deep Learning‐Based Techniques in Glioma Brain Tumor Segmentation Using Multi‐Parametric MRI: A Review on Clinical Applications and Future Outlooks;Journal of Magnetic Resonance Imaging;2024-07-29

3. Estimation of Fractal Dimension and Segmentation of Brain Tumor with Parallel Features Aggregation Network;Fractal and Fractional;2024-06-14

4. Marker-controlled watershed algorithm and fuzzy C-means clustering machine learning: automated segmentation of glioblastoma from MRI images in a case series;Annals of Medicine & Surgery;2024-01-26

5. LST-AI: A deep learning ensemble for accurate MS lesion segmentation;NeuroImage: Clinical;2024