Deep learning-based automatic tumor burden assessment of pediatric high-grade gliomas, medulloblastomas, and other leptomeningeal seeding tumors | Zendy

Jian Peng | Zendy; Daniel D Kim | Zendy; Jay Patel | Zendy; Xiaowei Zeng | Zendy; Jiaer Huang | Zendy; Ken Chang | Zendy; Xinping Xun | Zendy; Chen Zhang | Zendy; John Sollee | Zendy; Jing Wu | Zendy; Deepa Dalal | Zendy; Xue Feng | Zendy; Hao Zhou | Zendy; Chengzhang Zhu | Zendy; Beiji Zou | Zendy; Ke Jin | Zendy; Patrick Y. Wen | Zendy; Jerrold L. Boxerman | Zendy; Katherine E. Warren | Zendy; Tina Young Poussaint | Zendy; Lisa J. States | Zendy; Jayashree Kalpathy–Cramer | Zendy; Li Yang | Zendy; Raymond Y. Huang | Zendy; Harrison X. Bai | Zendy

Open Access

Deep learning-based automatic tumor burden assessment of pediatric high-grade gliomas, medulloblastomas, and other leptomeningeal seeding tumors

Author(s) -

Jian Peng,

Daniel D Kim,

Jay Patel,

Xiaowei Zeng,

Jiaer Huang,

Ken Chang,

Xinping Xun,

Chen Zhang,

John Sollee,

Jing Wu,

Deepa Dalal,

Xue Feng,

Hao Zhou,

Chengzhang Zhu,

Beiji Zou,

Ke Jin,

Patrick Y. Wen,

Jerrold L. Boxerman,

Katherine E. Warren,

Tina Young Poussaint,

Lisa J. States,

Jayashree Kalpathy–Cramer,

Li Yang,

Raymond Y. Huang,

Harrison X. Bai

Publication year - 2021

Publication title -

neuro-oncology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 4.005

H-Index - 125

eISSN - 1523-5866

pISSN - 1522-8517

DOI - 10.1093/neuonc/noab151

Subject(s) - medicine , magnetic resonance imaging , fluid attenuated inversion recovery , radiology , intraclass correlation , segmentation , nuclear medicine , ependymoma , neurosurgery , artificial intelligence , computer science , clinical psychology , psychometrics

Longitudinal measurement of tumor burden with magnetic resonance imaging (MRI) is an essential component of response assessment in pediatric brain tumors. We developed a fully automated pipeline for the segmentation of tumors in pediatric high-grade gliomas, medulloblastomas, and leptomeningeal seeding tumors. We further developed an algorithm for automatic 2D and volumetric size measurement of tumors.

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