IntroductionCerebral microbleeds (CMBs) are associated with white matter damage, and various neurodegenerative and cerebrovascular diseases. CMBs occur as small, circular hypointense lesions on T2*-weighted gradient recalled echo (GRE) and susceptibility-weighted imaging (SWI) images, and hyperintense on quantitative susceptibility mapping (QSM) images due to their paramagnetic nature. Accurate automated detection of CMBs would help to determine quantitative imaging biomarkers (e.g., CMB count) on large datasets. In this work, we propose a fully automated, deep learning-based, 3-step algorithm, using structural and anatomical properties of CMBs from any single input image modality (e.g., GRE/SWI/QSM) for their accurate detections.MethodsIn our method, the first step consists of an initial candidate detection step that detects CMBs with high sensitivity. In the second step, candidate discrimination step is performed using a knowledge distillation framework, with a multi-tasking teacher network that guides the student network to classify CMB and non-CMB instances in an offline manner. Finally, a morphological clean-up step further reduces false positives using anatomical constraints. We used four datasets consisting of different modalities specified above, acquired using various protocols and with a variety of pathological and demographic characteristics.ResultsOn cross-validation within datasets, our method achieved a cluster-wise true positive rate (TPR) of over 90% with an average of <2 false positives per subject. The knowledge distillation framework improves the cluster-wise TPR of the student model by 15%. Our method is flexible in terms of the input modality and provides comparable cluster-wise TPR and better cluster-wise precision compared to existing state-of-the-art methods. When evaluating across different datasets, our method showed good generalizability with a cluster-wise TPR >80 % with different modalities. The python implementation of the proposed method is openly available.

中文翻译：

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使用知识蒸馏框架自动检测 MR 图像上的脑微出血

简介脑微出血（CMB）与白质损伤以及各种神经退行性和脑血管疾病有关。CMB 在 T2* 加权梯度回波 (GRE) 和磁化率加权成像 (SWI) 图像上表现为小的圆形低信号病变，由于其顺磁性，在定量磁化率测绘 (QSM) 图像上表现为高信号。CMB 的准确自动检测将有助于确定大型数据集上的定量成像生物标志物（例如 CMB 计数）。在这项工作中，我们提出了一种完全自动化、基于深度学习的三步算法，利用来自任何单一输入图像模态（例如 GRE/SWI/QSM）的 CMB 的结构和解剖特性来进行准确检测。 ，第一步包括初始候选检测步骤，以高灵敏度检测 CMB。第二步，使用知识蒸馏框架执行候选区分步骤，并使用多任务教师网络指导学生网络以离线方式对 CMB 和非 CMB 实例进行分类。最后，形态清理步骤利用解剖约束进一步减少误报。我们使用了由上述指定的不同模式组成的四个数据集，这些数据集使用各种协议获取并具有各种病理和人口统计特征。结果在数据集中的交叉验证中，我们的方法实现了超过 90% 的集群真阳性率 (TPR)每个受试者平均<2个误报。知识蒸馏框架将学生模型的集群 TPR 提高了 15%。与现有最先进的方法相比，我们的方法在输入模式方面非常灵活，并且提供了可比较的聚类 TPR 和更好的聚类精度。当评估不同的数据集时，我们的方法表现出良好的通用性，不同模式下的集群 TPR > 80%。该方法的 python 实现是公开可用的。