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.

翻译：脑出血起病急骤，是一种严重威胁健康的疾病。在计算机断层扫描（CT）图像中快速准确地勾画出出血区域并估算出血量，有助于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构建了一种级联三维模型，对CT图像中的出血区域进行由粗到细的两阶段分割，并利用分割区域自动计算出血量。在包含341例脑出血CT扫描的数据集上，与传统的Tada公式相比，该模型在出血量估计方面提供了更高质量的分割结果，具有更高的准确率（DSC 85.66%）和更优的计算效率（每样本6.2秒）。