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Hemorrhagic Stroke (HS) has a rapid onset and is a serious condition thatposes a great health threat. Promptly and accurately delineating the bleedingregion and estimating the volume of bleeding in Computer Tomography (CT) imagescan assist clinicians in treatment planning, leading to improved treatmentoutcomes for patients. In this paper, a cascaded 3D model is constructed basedon UNet to perform a two-stage segmentation of the hemorrhage area in CT imagesfrom rough to fine, and the hemorrhage volume is automatically calculated fromthe segmented area. On a dataset with 341 cases of hemorrhagic stroke CT scans,the proposed model provides high-quality segmentation outcome with higheraccuracy (DSC 85.66%) and better computation efficiency (6.2 second per sample)when compared to the traditional Tada formula with respect to hemorrhage volumeestimation.

An Automatic Cascaded Model for Hemorrhagic Stroke Segmentation and Hemorrhagic Volume Estimation