Abstract
Lacunes or lacunar infarcts are small fluid-filled cavities associated with cerebral small vessel disease (cSVD). They contribute to the development of lacunar stroke, dementia, and gait impairment. The identification of lacunes is of great significance in elucidating the pathophysiological mechanism of cSVD. This paper proposes a semi-automated 3D multi-scale residual convolutional network (3D ResNet) for lacunar infarcts detection, which can learn global representations of the anatomical location of lacunes using two multi-scale magnetic resonance image modalities. This process requires minimal user intervention by passing the potential suspicious lacunes into the network. The proposed network is trained, validated, and tested using five-fold cross-validation using data, including 696 lacunes, from 288 subjects. We also present experiments on various combinations of multi-scale inputs and their effect on extracting global context features that directly influence identification performance. The proposed system shows its capability to differentiate between true lacunes and lacune mimics, providing supportive interpretations for neuroradiologists. The proposed 3D multi-scale ResNet identifies lacunar infarcts with a sensitivity of 96.41%, a specificity of 90.92%, an overall accuracy of 93.67%, and an area under the receiver operator characteristic curve (AUC) of 93.67% over all fold tests. The proposed system also achieved a precision of 91.40% and an average number of FPs per subject of 1.32. The system may be feasible for clinical use by supporting decision-making for lacunar infarct detection.
Highlights
Cerebral small vessel disease includes different abnormalities caused by a small vascular system in the brain, including lacunar infarcts, microbleeds, and white matter hyperintensities. cSVD is a leading cause of death, cognitive impairment, and functional loss worldwide [1]
This paper proposes a deep learning-based 3D residual convolutional network for lacunar infarcts detection from fluid-attenuated inversion recovery (FLAIR) and T1-MPRAGE MR brain images
This section presents the results of the proposed 3D multiscale Residual convolutional network (ResNet) relating to Lacunar infarct identification over five-fold cross-validation using the S7 multi-scale data combination
Summary
Cerebral small vessel disease (cSVD) includes different abnormalities caused by a small vascular system in the brain, including lacunar infarcts, microbleeds, and white matter hyperintensities. cSVD is a leading cause of death, cognitive impairment, and functional loss worldwide [1]. Cerebral small vessel disease (cSVD) includes different abnormalities caused by a small vascular system in the brain, including lacunar infarcts, microbleeds, and white matter hyperintensities. A type of cSVD, known as lacunes or lacunar strokes, are defined as small lesions caused by the blockage of penetrating branches of the main cerebral arteries. This occlusion causes a lack of oxygen in the brain tissues, The associate editor coordinating the review of this manuscript and approving it for publication was Lefei Zhang. CSVD can be visualized using magnetic resonance imaging (MRI) and computed tomography (CT) [4], [5]. Fluid-attenuated inversion recovery (FLAIR) and T1-weighted magnetization-prepared
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