作者
Anahita Fathi Kazerooni, Sherjeel Arif, Rachel Madhogarhia, Nastaran Khalili, Debanjan Haldar, Sina Bagheri, Ariana M Familiar, Hannah Anderson, Shuvanjan Haldar, Wenxin Tu, Meen Chul Kim, Karthik Viswanathan, Sabine Muller, Michael Prados, Cassie Kline, Lorenna Vidal, Mariam Aboian, Phillip B Storm, Adam C Resnick, Jeffrey B Ware, Arastoo Vossough, Christos Davatzikos, Ali Nabavizadeh
发表日期
2023/1/1
期刊
Neuro-Oncology Advances
卷号
5
期号
1
页码范围
vdad027
出版商
Oxford University Press
简介
Background
Brain tumors are the most common solid tumors and the leading cause of cancer-related death among all childhood cancers. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high interoperator variability. We present a multi-institutional deep learning-based method for automated brain extraction and segmentation of pediatric brain tumors based on multi-parametric MRI scans.
Methods
Multi-parametric scans (T1w, T1w-CE, T2, and T2-FLAIR) of 244 pediatric patients (n = 215 internal and n = 29 external cohorts) with de novo brain tumors, including a variety of tumor subtypes, were preprocessed and manually segmented to identify the brain tissue and tumor subregions into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing …
学术搜索中的文章
A Fathi Kazerooni, S Arif, R Madhogarhia, N Khalili… - Neuro-Oncology Advances, 2023