Deep-learned 3D black-blood imaging using automatic labelling technique and 3D convolutional neural networks for detecting metastatic brain tumors

Yohan Jun, Taejoon Eo, Taeseong Kim, Hyungseob Shin, Dosik Hwang, So Hi Bae, Yae Won Park, Ho Joon Lee, Byoung Wook Choi, Sung Soo Ahn

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Black-blood (BB) imaging is used to complement contrast-enhanced 3D gradient-echo (CE 3D-GRE) imaging for detecting brain metastases, requiring additional scan time. In this study, we proposed deep-learned 3D BB imaging with an auto-labelling technique and 3D convolutional neural networks for brain metastases detection without additional BB scan. Patients were randomly selected for training (29 sets) and testing (36 sets). Two neuroradiologists independently evaluated deep-learned and original BB images, assessing the degree of blood vessel suppression and lesion conspicuity. Vessel signals were effectively suppressed in all patients. The figure of merits, which indicate the diagnostic performance of radiologists, were 0.9708 with deep-learned BB and 0.9437 with original BB imaging, suggesting that the deep-learned BB imaging is highly comparable to the original BB imaging (difference was not significant; p = 0.2142). In per patient analysis, sensitivities were 100% for both deep-learned and original BB imaging; however, the original BB imaging indicated false positive results for two patients. In per lesion analysis, sensitivities were 90.3% for deep-learned and 100% for original BB images. There were eight false positive lesions on the original BB imaging but only one on the deep-learned BB imaging. Deep-learned 3D BB imaging can be effective for brain metastases detection.

Original languageEnglish
Article number9450
JournalScientific reports
Volume8
Issue number1
DOIs
Publication statusPublished - 2018 Dec 1

Bibliographical note

Funding Information:
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (2016R1A2B4015016) and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2017R1D1A1B03030440).

All Science Journal Classification (ASJC) codes

  • General

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