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Deep Learning-Based Assessment of Cerebral Microbleeds in COVID-19

2023-01-23 08:46:17
Neus Rodeja Ferrer, Malini Vendela Sagar, Kiril Vadimovic Klein, Christina Kruuse, Mads Nielsen, Mostafa Mehdipour Ghazi

Abstract

Cerebral Microbleeds (CMBs), typically captured as hypointensities from susceptibility-weighted imaging (SWI), are particularly important for the study of dementia, cerebrovascular disease, and normal aging. Recent studies on COVID-19 have shown an increase in CMBs of coronavirus cases. Automatic detection of CMBs is challenging due to the small size and amount of CMBs making the classes highly imbalanced, lack of publicly available annotated data, and similarity with CMB mimics such as calcifications, irons, and veins. Hence, the existing deep learning methods are mostly trained on very limited research data and fail to generalize to unseen data with high variability and cannot be used in clinical setups. To this end, we propose an efficient 3D deep learning framework that is actively trained on multi-domain data. Two public datasets assigned for normal aging, stroke, and Alzheimer's disease analysis as well as an in-house dataset for COVID-19 assessment are used to train and evaluate the models. The obtained results show that the proposed method is robust to low-resolution images and achieves 78% recall and 80% precision on the entire test set with an average false positive of 1.6 per scan.

Abstract (translated)

脑microbleeds(CMBs)通常从脆性加权成像(SWI)中捕获为低密度值,对于研究脑损伤、脑动脉硬化和正常 aging 特别重要。最近的研究对 COVID-19 进行了研究,发现 CMBs 病例中的 CMBs 数量增加。自动检测 CMBs 具有挑战性,因为 CMBs 的数量和大小使分类非常不平衡,缺乏公开标注数据,以及与 CMB 模仿物(如骨质疏松、铁和血管)相似的相似性。因此,现有的深度学习方法大多基于非常有限的研究数据进行训练,无法泛化到未观测到的具有高变异性的数据,并且无法在临床实验中使用。为此,我们提出了一种高效的 3D 深度学习框架,它 actively train s on multi-domain data。将两个用于正常年龄、中风和阿尔茨海默病分析的公共数据集以及用于 COVID-19 评估的公司内部数据集用于训练和评估模型。训练和评估的结果表明,该方法对低分辨率图像具有鲁棒性,在测试集上实现 78% 的召回率和 80% 的精度,扫描平均出现 1.6 个假阳性。

URL

https://arxiv.org/abs/2301.09322

PDF

https://arxiv.org/pdf/2301.09322.pdf


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