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 0.96 ± 0.03 (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.

中文翻译：

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基于深度学习的脑转移检测和分段辅助管理。

背景技术三维T1磁化准备的快速采集梯度回波（3D-T1-MPRAGE）在检测MRI中的脑转移（BM）方面是首选。我们为3D-T1-MPRAGE图像上的BM（称为BMDS网络）开发了一种基于深度学习的自动检测和分割方法，并对其性能进行了评估。方法BMDS网络是级联的3D全卷积网络（FCN），用于自动检测和分割BM。总共纳入了3652家医院的1652例3D-T1-MPRAGE图像患者（分别为n = 1201、231和220）。由神经放射科医生和放射肿瘤学家在3D-T1-MPRAGE图像的共识阅读中获得了手动分割。评估了分割的敏感性，特异性和骰子比。特异性和敏感性可测量相关分段体素的分数。骰子比率用于定量测量自动和手动分割结果之间的重叠。配对样本的t检验和方差分析用于统计分析。结果BMDS网络可以检测所有BM，从而提供100％的准确度的检测结果。通过对1201名患者进行数据集的4倍交叉验证，自动分割与手动分割密切相关：灵敏度为0.96±0.03（范围0.84-0.99），特异性为0.99±0.0002（范围0.99-1.00）和总肿瘤体积的骰子比率为0.85±0.08（范围0.62-0.95）。在其他2个数据集上的类似性能也证明了BMDS网络在各种设置下正确检测和分割BM的鲁棒性。