Robust performance of deep learning for automatic detection and segmentation of brain metastases using three-dimensional black-blood and three-dimensional gradient echo imaging | Zendy

Yae Won Park | Zendy; Yohan Jun | Zendy; Yangho Lee | Zendy; Kyunghwa Han | Zendy; Chansik An | Zendy; Sung Soo Ahn | Zendy; Dosik Hwang | Zendy; SeungKoo Lee | Zendy

Open Access

Robust performance of deep learning for automatic detection and segmentation of brain metastases using three-dimensional black-blood and three-dimensional gradient echo imaging

Author(s) -

Yae Won Park,

Yohan Jun,

Yangho Lee,

Kyunghwa Han,

Chansik An,

Sung Soo Ahn,

Dosik Hwang,

SeungKoo Lee

Publication year - 2021

Publication title -

european radiology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.606

H-Index - 149

eISSN - 1432-1084

pISSN - 0938-7994

DOI - 10.1007/s00330-021-07783-3

Subject(s) - medicine , neuroradiology , sørensen–dice coefficient , brain metastasis , 3d ultrasound , segmentation , nuclear medicine , gradient echo , radiology , metastasis , magnetic resonance imaging , ultrasound , cancer , image segmentation , artificial intelligence , neurology , computer science , psychiatry

To evaluate whether a deep learning (DL) model using both three-dimensional (3D) black-blood (BB) imaging and 3D gradient echo (GRE) imaging may improve the detection and segmentation performance of brain metastases compared to that using only 3D GRE imaging.

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