Background and Objective

: Cerebral microbleeds (CMB) are important biomarkers of cerebrovascular diseases and cognitive dysfunctions. Susceptibility weighted imaging (SWI) is a common MRI sequence where CMB appear as small hypointense blobs. The prevalence of CMB in the population and in each scan is low, resulting in tedious and time-consuming visual assessment. Automated detection methods would be of value but are challenged by the CMB low prevalence, the presence of mimics such as blood vessels, and the difficulty to obtain sufficient ground truth for training and testing. In this paper, synthetic CMB (sCMB) generation using an analytical model is proposed for training and testing machine learning methods. The main aim is creating perfect synthetic ground truth as similar as reals, in high number, with a high diversity of shape, volume, intensity, and location to improve training of supervised methods.

Method

: sCMB were modelled with a random Gaussian shape and added to healthy brain locations. We compared training on our synthetic data to standard augmentation techniques. We performed a validation experiment using sCMB and report result for whole brain detection using a 10-fold cross validation design with an ensemble of 10 neural networks.

Results

: Performance was close to state of the art (~9 false positives per scan), when random forest was trained on synthetic only and tested on real lesion. Other experiments showed that top detection performance could be achieved when training on synthetic CMB only. Our dataset is made available, including a version with 37,000 synthetic lesions, that could be used for benchmarking and training.

Conclusion

: Our proposed synthetic microbleeds model is a powerful data augmentation approach for CMB classification with and should be considered for training automated lesion detection system from MRI SWI.

中文翻译：

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用于分类器训练的合成微出血生成，无需地面实况

背景与目的

：脑微出血 (CMB) 是脑血管疾病和认知功能障碍的重要生物标志物。磁敏感加权成像 (SWI) 是一种常见的 MRI 序列，其中 CMB 表现为小的低信号斑点。CMB 在人群中和每次扫描中的流行率很低，导致视觉评估繁琐耗时。自动检测方法将是有价值的，但受到 CMB 低流行率、血管等模拟物的存在以及难以获得足够的训练和测试的基本事实的挑战。在本文中，提出了使用分析模型的合成 CMB (sCMB) 生成来训练和测试机器学习方法。主要目标是创造完美的合成地面实况，与真实物体相似，数量多，形状、体积、强度、

方法

： sCMB 使用随机高斯形状建模并添加到健康的大脑位置。我们将合成数据的训练与标准增强技术进行了比较。我们使用 sCMB 进行了验证实验，并使用 10 倍交叉验证设计和 10 个神经网络的集合报告了全脑检测的结果。

结果

：当随机森林仅在合成上进行训练并在真实病变上进行测试时，性能接近最先进的水平（每次扫描约 9 次误报）。其他实验表明，仅在合成 CMB 上进行训练时可以实现顶级检测性能。我们提供了数据集，包括一个包含 37,000 个合成病变的版本，可用于基准测试和培训。

结论

：我们提出的合成微出血模型是一种强大的 CMB 分类数据增强方法，应考虑用于训练来自 MRI SWI 的自动病变检测系统。