Precise and fast prediction methods for ischemic areas (core and penumbra) in acute ischemic stroke (AIS) patients are of significant clinical interest: they play an essential role in improving diagnosis and treatment planning. Computed Tomography (CT) scan is one of the primary modalities for early assessment in patients with suspected AIS. CT Perfusion (CTP) is often used as a primary assessment to determine stroke location, severity, and volume of ischemic lesions. Current automatic segmentation methods for CTP mostly use already processed 3D color maps conventionally used for visual assessment by radiologists as input. Alternatively, the raw CTP data is used on a slice-by-slice basis as 2D+time input, where the spatial information over the volume is ignored. In this paper, we investigate different methods to utilize the entire 4D CTP as input to fully exploit the spatio-temporal information. This leads us to propose a novel 4D convolution layer. Our comprehensive experiments on a local dataset comprised of 152 patients divided into three groups show that our proposed models generate more precise results than other methods explored. A Dice Coefficient of 0.70 and 0.45 is achieved for penumbra and core areas, respectively. The code is available on https://github.com/Biomedical-Data-Analysis-Laboratory/4D-mJ-Net.git.
翻译:急性缺血性中风(AIS)病人的缺血区(核心和阴膜)的预测和快速预测方法具有重大临床利益:他们在改善诊断和治疗规划方面发挥着至关重要的作用。 合成托盘学(CT)扫描是怀疑有AIS的病人进行早期评估的主要方式之一。 CT Pervolut(CTTP)经常被用作确定中风地点、严重程度和无化学病量的主要评估手段。 CTP目前的自动分解方法主要使用已经处理过的3D色图,由放射科医生传统用来进行视觉评估。 或者,原始的CTP数据在切片切片切除法基础上使用,作为2D+时间输入,其中对卷内的空间信息置之不理。 在本文中,我们调查使用整个4DCTP作为投入以充分利用口腔温度信息、严重程度和体温值信息量的各种方法。这导致我们提出一个新的4D变压层。我们对由152名病人组成的当地数据集进行的全面实验分为三组。我们提议的模型比其他方法更精确地产生结果。ADQ-DQam-Dammasalalal-Damalal-Damal-commabreab 。</s>