An Automatic Cascaded Model for Hemorrhagic Stroke Segmentation and
  Hemorrhagic Volume Estimation
Weijin Xu Zhuang Sha Huihua Yang Rongcai Jiang Zhanying Li Wentao Liu Ruisheng Su
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.