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

Authors
 Yae Won Park  ;  Yohan Jun  ;  Yangho Lee  ;  Kyunghwa Han  ;  Chansik An  ;  Sung Soo Ahn  ;  Dosik Hwang  ;  Seung-Koo Lee 
Citation
 EUROPEAN RADIOLOGY, Vol.31(9) : 6686-6695, 2021-09 
Journal Title
EUROPEAN RADIOLOGY
ISSN
 0938-7994 
Issue Date
2021-09
MeSH
African Americans ; Brain Neoplasms* / diagnostic imaging ; Contrast Media ; Deep Learning* ; Humans ; Imaging, Three-Dimensional ; Magnetic Resonance Imaging
Keywords
Artificial intelligence ; Deep learning ; Magnetic resonance imaging ; Neoplasm metastasis
Abstract
Objectives: 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. Methods: A total of 188 patients with brain metastases (917 lesions) who underwent a brain metastasis MRI protocol including contrast-enhanced 3D BB and 3D GRE were included in the training set. DL models based on 3D U-net were constructed. The models were validated in the test set consisting of 45 patients with brain metastases (203 lesions) and 49 patients without brain metastases. Results: The combined 3D BB and 3D GRE model yielded better performance than the 3D GRE model (sensitivities of 93.1% vs 76.8%, p < 0.001), and this effect was significantly stronger in subgroups with small metastases (p interaction < 0.001). For metastases < 3 mm, ≥ 3 mm and < 10 mm, and ≥ 10 mm, the sensitivities were 82.4%, 93.2%, and 100%, respectively. The combined 3D BB and 3D GRE model showed a false-positive per case of 0.59 in the test set. The combined 3D BB and 3D GRE model showed a Dice coefficient of 0.822, while 3D GRE model showed a lower Dice coefficient of 0.756. Conclusions: The combined 3D BB and 3D GRE DL model may improve the detection and segmentation performance of brain metastases, especially in detecting small metastases. Key points: • The combined 3D BB and 3D GRE model yielded better performance for the detection of brain metastases than the 3D GRE model (p < 0.001), with sensitivities of 93.1% and 76.8%, respectively. • The combined 3D BB and 3D GRE model showed a false-positive rate per case of 0.59 in the test set. • The combined 3D BB and 3D GRE model showed a Dice coefficient of 0.822, while the 3D GRE model showed a lower Dice coefficient of 0.756.
Full Text
https://link.springer.com/article/10.1007%2Fs00330-021-07783-3
DOI
10.1007/s00330-021-07783-3
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Park, Yae Won(박예원) ORCID logo https://orcid.org/0000-0001-8907-5401
Ahn, Sung Soo(안성수) ORCID logo https://orcid.org/0000-0002-0503-5558
Lee, Seung Koo(이승구) ORCID logo https://orcid.org/0000-0001-5646-4072
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/185956
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