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.

基于 UNet 构建了一个级联的三维模型，从粗到细对 CT 图像中的出血区域进行两阶段分割，并自动计算出出血体积，与传统的 Tada 公式相比，该模型在 341 例出血性中风 CT 扫描中提供了高质量的分割结果（DSC 85.66%）和更好的计算效率（每个样本 6.2 秒）。