<![CDATA[Background: Head CT Scans for hemorrhage cases at the Radiology Unit of Aisyiyah Ponorogo Hospital use an automated volumetric method to calculate the volume of bleeding. This process involves segmenting the bleeding area, and the volume results are immediately available without manual calculations. Unlike some studies that suggest automated methods require more time, this method was chosen at Aisyiyah Ponorogo Hospital because it utilizes multislice CT Scanning technology, which allows for faster and more accurate estimation of the volume of bleeding. Methods: This study employed a descriptive qualitative approach with observation, interviews, and documentation techniques. The subjects included three radiographers and one radiologist. The study took place at the Radiology Unit of Aisyiyah Ponorogo General Hospital from March to July 2025. Data were collected through direct observation, in-depth interviews, image documentation, and literature review. Data analysis was conducted through transcription, categorization, and narrative presentation based on the research focus. Results: There are differences in the preparation of tools and materials used in bleeding cases. Calculating bleeding volume using automated segmentation-based methods still requires manual involvement and precision in shading the bleeding volume. However, automated methods remain superior because they reduce subjectivity between operators and provide consistent, measurable, accurate, efficient, and reproducible results. Calculating bleeding volume using automated methods is particularly helpful in emergency situations, where clinical decisions must be made quickly. Conclusions: Head CT Scans for hemorrhage cases have not optimized the use of head straps and blankets. The use of blankets and head straps can provide comfort and reduce movement during the examination. Automatic segmentation-based hemorrhage volume calculation improves work efficiency, result consistency, and diagnostic accuracy, while reducing the potential for subjective errors similar to manual methods. This method has nearly perfect agreement with manual methods (coefficient of 0.99), making it worthy of inclusion in medical assistive technology]]>
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