An Automatic Cascaded Model for Hemorrhagic Stroke Segmentation and Hemorrhagic Volume Estimation
This work addresses the need for rapid and accurate stroke analysis to assist clinicians in treatment planning, though it appears incremental as it builds on UNet with a cascaded approach.
The authors tackled the problem of segmenting hemorrhagic stroke regions and estimating bleeding volume in CT images, achieving a Dice Similarity Coefficient (DSC) of 85.66% and a computation time of 6.2 seconds per sample.
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.