Electrical and Computer Systems Engineering Discipline, School of Engineering, Monash University Malaysia, Bandar Sunway, Malaysia.
Radiology Department, Sunway Medical Centre, Bandar Sunway, Malaysia.
J Magn Reson Imaging. 2023 Jun;57(6):1728-1740. doi: 10.1002/jmri.28456. Epub 2022 Oct 8.
Research suggests that treatment of multiple brain metastases (BMs) with stereotactic radiosurgery shows improvement when metastases are detected early, providing a case for BM detection capabilities on small lesions.
To demonstrate automatic detection of BM on three MRI datasets using a deep learning-based approach. To improve the performance of the network is iteratively co-trained with datasets from different domains. A systematic approach is proposed to prevent catastrophic forgetting during co-training.
Retrospective.
A total of 156 patients (105 ground truth and 51 pseudo labels) with 1502 BM (BrainMetShare); 121 patients with 722 BM (local); 400 patients with 447 primary gliomas (BrATS). Training/pseudo labels/validation data were distributed 84/51/21 (BrainMetShare). Training/validation data were split: 121/23 (local) and 375/25 (BrATS).
FIELD STRENGTH/SEQUENCE: A 5 T and 3 T/T1 spin-echo postcontrast (T1-gradient echo) (BrainMetShare), 3 T/T1 magnetization prepared rapid acquisition gradient echo postcontrast (T1-MPRAGE) (local), 0.5 T, 1 T, and 1.16 T/T1-weighted-fluid-attenuated inversion recovery (T1-FLAIR) (BrATS).
The ground truth was manually segmented by two (BrainMetShare) and four (BrATS) radiologists and manually annotated by one (local) radiologist. Confidence and volume based domain adaptation (CAVEAT) method of co-training the three datasets on a 3D nonlocal convolutional neural network (CNN) architecture was implemented to detect BM.
The performance was evaluated using sensitivity and false positive rates per patient (FP/patient) and free receiver operating characteristic (FROC) analysis at seven predefined (1/8, 1/4, 1/2, 1, 2, 4, and 8) FPs per scan.
The sensitivity and FP/patient from a held-out set registered 0.811 at 2.952 FP/patient (BrainMetShare), 0.74 at 3.130 (local), and 0.723 at 2.240 (BrATS) using the CAVEAT approach with lesions as small as 1 mm being detected.
Improved sensitivities at lower FP can be achieved by co-training datasets via the CAVEAT paradigm to address the problem of data sparsity.
3 TECHNICAL EFFICACY STAGE: 2.
研究表明，对于多发性脑转移瘤（BM）的治疗，采用立体定向放射外科治疗时，若能早期发现转移瘤，则效果会有所改善，这就为在小病灶上检测 BM 提供了可能。
使用基于深度学习的方法，在三个 MRI 数据集上演示 BM 的自动检测。通过迭代式的与不同领域的数据集共同训练，以提高网络的性能。提出了一种系统的方法来防止共同训练期间的灾难性遗忘。
回顾性。
共有 156 名患者（105 名有明确诊断，51 名带有伪标签），共 1502 个 BM（BrainMetShare）；121 名患者，722 个 BM（local）；400 名患者，447 个原发性脑肿瘤（BrATS）。训练/伪标签/验证数据的分布为 84/51/21（BrainMetShare）。训练/验证数据的分割：121/23（local）和 375/25（BrATS）。
场强/序列：5T 和 3T/T1 自旋回波对比后（T1-梯度回波）（BrainMetShare），3T/T1 磁化准备快速获取梯度回波对比后（T1-MPRAGE）（local），0.5T，1T 和 1.16T/T1 加权液体衰减反转恢复（T1-FLAIR）（BrATS）。
ground truth 由两位（BrainMetShare）和四位（BrATS）放射科医生手动分割，由一位（local）放射科医生手动标注。使用置信度和基于体积的领域自适应（CAVEAT）方法，在一个 3D 非局部卷积神经网络（CNN）架构上对三个数据集进行共同训练，以检测 BM。
使用灵敏度和每位患者的假阳性率（FP/patient）以及七个预定义（1/8、1/4、1/2、1、2、4 和 8）扫描 FP 下的免费接收器操作特征（FROC）分析，对预留数据集进行评估。
使用 CAVEAT 方法，在 BrainMetShare 中，0.811 的灵敏度和 2.952 FP/patient 的 FP/patient，在 local 中为 0.74 和 3.130 FP/patient，在 BrATS 中为 0.723 和 2.240 FP/patient，可检测到 1mm 大小的病灶。
通过 CAVEAT 范式进行数据集的共同训练，可以提高数据稀疏性问题的灵敏度，并降低假阳性率。
3 技术功效分期：2。
