期刊
NEURO-ONCOLOGY
卷 22, 期 4, 页码 505-514出版社
OXFORD UNIV PRESS INC
DOI: 10.1093/neuonc/noz234
关键词
brain metastases; deep learning; fully convolution network; MRI; stereotactic radiotherapy
资金
- National Natural Science Foundation of China [61802234, 61876101, 61971271]
- Natural Science Foundation of Shandong Province [ZR2019QF007]
- China Postdoctoral Project [2017M612339]
- Key Technologies RAMP
- D Program of Shandong Province [2017CXCG1209]
- Taishan Scholars Program [tsqn20161070]
Background. Three-dimensional T1 magnetization prepared rapid acquisition gradient echo (3D-T1-MPRAGE) is preferred in detecting brain metastases (BM) among MRI. We developed an automatic deep learning-based detection and segmentation method for BM (named BMDS net) on 3D-T1-MPRAGE images and evaluated its performance. Methods. The BMDS net is a cascaded 3D fully convolution network (FCN) to automatically detect and segment BM. In total, 1652 patients with 3D-T1-MPRAGE images from 3 hospitals (n = 1201, 231, and 220, respectively) were retrospectively included. Manual segmentations were obtained by a neuroradiologist and a radiation oncologist in a consensus reading in 3D-T1-MPRAGE images. Sensitivity, specificity, and dice ratio of the segmentation were evaluated. Specificity and sensitivity measure the fractions of relevant segmented voxels. Dice ratio was used to quantitatively measure the overlap between automatic and manual segmentation results. Paired samples t-tests and analysis of variance were employed for statistical analysis. Results. The BMDS net can detect all BM, providing a detection result with an accuracy of 100%. Automatic segmentations correlated strongly with manual segmentations through 4-fold cross-validation of the dataset with 1201 patients: the sensitivity was 0.960.03 (range, 0.84-0.99), the specificity was 0.99 +/- 0.0002 (range, 0.99-1.00), and the dice ratio was 0.85 +/- 0.08 (range, 0.62-0.95) for total tumor volume. Similar performances on the other 2 datasets also demonstrate the robustness of BMDS net in correctly detecting and segmenting BM in various settings. Conclusions. The BMDS net yields accurate detection and segmentation of BM automatically and could assist stereotactic radiotherapy management for diagnosis, therapy planning, and follow-up.
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