用于出血性脑卒中分割与出血体积估计的自动级联模型
An Automatic Cascaded Model for Hemorrhagic Stroke Segmentation and Hemorrhagic Volume Estimation
AI总结：
本文基于UNet构建级联3D模型，实现CT图像中出血区域由粗到精的两阶段分割并自动计算出血量，在341例数据集上验证其较传统Tada公式具有更高的分割准确率和计算效率。
摘要：
出血性脑卒中（HS）发病急促且病情严重，对健康构成巨大威胁。在计算机断层扫描（CT）图像中迅速且准确地勾勒出血区域并估算出血量，能够协助临床医生制定治疗方案，从而改善患者的治疗效果。本文基于UNet构建了一个级联3D模型，对CT图像中的出血区域进行由粗到精的两阶段分割，并根据分割出的区域自动计算出血量。在包含341例出血性脑卒中CT扫描的数据集上，所提出的模型提供了高质量的分割结果，在出血体积估计方面，与传统Tada公式相比，具有更高的准确率（DSC 85.66%）和更好的计算效率（每样本6.2秒）。
英文摘要：
Hemorrhagic Stroke (HS) has a rapid onset and is a serious condition that poses a great health threat. Promptly and accurately delineating the bleeding region and estimating the volume of bleeding in Computer Tomography (CT) images can assist clinicians in treatment planning, leading to improved treatment outcomes for patients. In this paper, a cascaded 3D model is constructed based on UNet to perform a two-stage segmentation of the hemorrhage area in CT images from rough to fine, and the hemorrhage volume is automatically calculated from the segmented area. On a dataset with 341 cases of hemorrhagic stroke CT scans, the proposed model provides high-quality segmentation outcome with higher accuracy (DSC 85.66%) and better computation efficiency (6.2 second per sample) when compared to the traditional Tada formula with respect to hemorrhage volume estimation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
