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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

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dc.contributor.author박예원-
dc.contributor.author안성수-
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dc.date.accessioned2021-11-19T01:38:09Z-
dc.date.available2021-11-19T01:38:09Z-
dc.date.issued2021-09-
dc.identifier.issn0938-7994-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/185956-
dc.description.abstractObjectives: 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.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherSpringer International-
dc.relation.isPartOfEUROPEAN RADIOLOGY-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHAfrican Americans-
dc.subject.MESHBrain Neoplasms* / diagnostic imaging-
dc.subject.MESHContrast Media-
dc.subject.MESHDeep Learning*-
dc.subject.MESHHumans-
dc.subject.MESHImaging, Three-Dimensional-
dc.subject.MESHMagnetic Resonance Imaging-
dc.titleRobust performance of deep learning for automatic detection and segmentation of brain metastases using three-dimensional black-blood and three-dimensional gradient echo imaging-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiology (영상의학교실)-
dc.contributor.googleauthorYae Won Park-
dc.contributor.googleauthorYohan Jun-
dc.contributor.googleauthorYangho Lee-
dc.contributor.googleauthorKyunghwa Han-
dc.contributor.googleauthorChansik An-
dc.contributor.googleauthorSung Soo Ahn-
dc.contributor.googleauthorDosik Hwang-
dc.contributor.googleauthorSeung-Koo Lee-
dc.identifier.doi10.1007/s00330-021-07783-3-
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dc.relation.journalcodeJ00851-
dc.identifier.eissn1432-1084-
dc.identifier.pmid33738598-
dc.identifier.urlhttps://link.springer.com/article/10.1007%2Fs00330-021-07783-3-
dc.subject.keywordArtificial intelligence-
dc.subject.keywordDeep learning-
dc.subject.keywordMagnetic resonance imaging-
dc.subject.keywordNeoplasm metastasis-
dc.contributor.alternativeNamePark, Yae-Won-
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dc.citation.volume31-
dc.citation.number9-
dc.citation.startPage6686-
dc.citation.endPage6695-
dc.identifier.bibliographicCitationEUROPEAN RADIOLOGY, Vol.31(9) : 6686-6695, 2021-09-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers

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