Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China; Department of Clinical Neuroscience, the University of Calgary, Calgary, Alberta, Canada.
Department of Clinical Neuroscience, the University of Calgary, Calgary, Alberta, Canada; Department of Radiology, the University of Calgary, Calgary, Alberta, Canada.
Med Image Anal. 2021 May;70:101984. doi: 10.1016/j.media.2021.101984. Epub 2021 Feb 23.
Detecting early infarct (EI) plays an essential role in patient selection for reperfusion therapy in the management of acute ischemic stroke (AIS). EI volume at acute or hyper-acute stage can be measured using advanced pre-treatment imaging, such as MRI and CT perfusion. In this study, a novel multi-task learning approach, EIS-Net, is proposed to segment EI and score Alberta Stroke Program Early CT Score (ASPECTS) simultaneously on baseline non-contrast CT (NCCT) scans of AIS patients. The EIS-Net comprises of a 3D triplet convolutional neural network (T-CNN) for EI segmentation and a multi-region classification network for ASPECTS scoring. T-CNN has triple encoders with original NCCT, mirrored NCCT, and atlas as inputs, as well as one decoder. A comparison disparity block (CDB) is designed to extract and enhance image contexts. In the decoder, a multi-level attention gate module (MAGM) is developed to recalibrate the features of the decoder for both segmentation and classification tasks. Evaluations using a high-quality dataset comprising of baseline NCCT and concomitant diffusion weighted MRI (DWI) as reference standard of 260 patients with AIS show that the proposed EIS-Net can accurately segment EI. The EIS-Net segmented EI volume strongly correlates with EI volume on DWI (r=0.919), and the mean difference between the two volumes is 8.5 mL. For ASPECTS scoring, the proposed EIS-Net achieves an intraclass correlation coefficient of 0.78 for total 10-point ASPECTS and a kappa of 0.75 for dichotomized ASPECTS (≤ 4 vs. >4). Both EI segmentation and ASPECTS scoring tasks achieve state-of-the-art performances.
检测早期梗死（EI）在急性缺血性脑卒中（AIS）管理中对选择再灌注治疗的患者起着至关重要的作用。在急性或超急性期，可以使用先进的预处理成像（如 MRI 和 CT 灌注）来测量 EI 容积。在这项研究中，提出了一种新的多任务学习方法 EIS-Net，用于同时在 AIS 患者的基线非对比 CT（NCCT）扫描上分割 EI 和评分 Alberta Stroke Program Early CT Score（ASPECTS）。EIS-Net 由一个 3D 三重卷积神经网络（T-CNN）用于 EI 分割和一个多区域分类网络用于 ASPECTS 评分组成。T-CNN 具有三重编码器，输入为原始 NCCT、镜像 NCCT 和图谱，以及一个解码器。设计了一个比较差异块（CDB）来提取和增强图像上下文。在解码器中，开发了一个多级注意门模块（MAGM），用于重新校准解码器的特征，以适应分割和分类任务。使用包含 260 名 AIS 患者的基线 NCCT 和伴随的弥散加权 MRI（DWI）作为参考标准的高质量数据集进行评估表明，所提出的 EIS-Net 可以准确地分割 EI。EIS-Net 分割的 EI 容积与 DWI 上的 EI 容积具有很强的相关性（r=0.919），两个容积之间的平均差异为 8.5 mL。对于 ASPECTS 评分，所提出的 EIS-Net 对总 10 分 ASPECTS 的组内相关系数为 0.78，对二分 ASPECTS（≤4 与>4）的kappa 为 0.75。EI 分割和 ASPECTS 评分任务均达到了最先进的性能。
