Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors-Reference-Cited by-同舟云学术

Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors

Published:2024-05-09 Issue:8 Volume:45 Page:1081-1089
ISSN:0195-6108
Container-title:American Journal of Neuroradiology
language:en
Short-container-title:AJNR Am J Neuroradiol

Author:

Vossough Arastoo^ORCID,Khalili Nastaran,Familiar Ariana M.^ORCID,Gandhi Deep,Viswanathan Karthik,Tu Wenxin^ORCID,Haldar Debanjan,Bagheri Sina^ORCID,Anderson Hannah,Haldar Shuvanjan,Storm Phillip B.,Resnick Adam,Ware Jeffrey B.^ORCID,Nabavizadeh Ali^ORCID,Fathi Kazerooni Anahita^ORCID

Funder

DIPG/DMG Research Funding Alliance (DDRFA) and Pediatric Brain Tumor Foundation

Advancing Image Data Interoperability and Standards within an NIH Ecosystem (AIDISNE): A CHOP, FlyWheel, and Seven Bridges Integration Demonstration Project

NCI Cancer Informatics Scholar

Publisher

American Society of Neuroradiology (ASNR)

Reference36 articles.

1. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2009–2013

2. Pediatric Brain Tumors

3. Pediatric low-grade glioma: Targeted therapeutics and clinical trials in the molecular era

4. Comparison of manual and automatic segmentation methods for brain structures in the presence of space-occupying lesions: a multi-expert study

5. WHO Classification of Central Nervous System Tumours. Lyons, France: International Agency for Research on Cancer; 2021

Cited by 2 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. Automated Pediatric Brain Tumor Imaging Assessment Tool from CBTN: Enhancing Suprasellar Region Inclusion and Managing Limited Data with Deep Learning;2024-07-31

2. Towards consistency in pediatric brain tumor measurements: Challenges, solutions, and the role of artificial intelligence-based segmentation;Neuro-Oncology;2024-05-21