Hemoperitoneum Quantification in Non-contrast CT: Evaluating Feasibility with the Novel HUVAO Segmentation Algorithm

Abstract Background  Injuries involving substantial bleeding, frequently encountered in victims of road traffic accidents, pose a significant risk to mortality. For abdominal trauma cases, accurately assessing internal bleeding and hematomas becomes crucial. Detecting hemoperitoneum, which indicates both blood loss and organ damage in the abdominal cavity, requires precise evaluation. Timely diagnosis and quantification of hemoperitoneum following road accidents are crucial during the critical golden hour, enabling prioritized medical intervention and potentially saving lives while enhancing overall patient care. However, achieving precise hemoperitoneum quantification in abdominal trauma faces challenges due to the intricate nature of overlapping Hounsfield unit (HU) regions. Methods  In this feasibility study, we sought to assess the efficacy of the novel HUVAO (Hounsfield Unit-based Volume quantification of Asymmetrical Objects) segmentation algorithm for quantifying hemoperitoneum in thoracoabdominal non-contrast computed tomography (CT) images. Using 28 retrospective non-contrast CT scans of thoracoabdominal regions from trauma patients, we analyzed crucial imaging data without necessitating additional scans or contrast-enhanced procedures. The study aimed to compare HUVAO against classical algorithms and visual estimations by trained radiologists for hemoperitoneum segmentation in thoracoabdominal non-contrast CT images. Results  Our findings revealed that although the technical feasibility of employing HUVAO and other segmentation algorithms for hemoperitoneum quantification is evident, the outcomes derived from these algorithms display notable discrepancies. Conclusion  In assessing technical feasibility, we introduced the HUVAO segmentation algorithm for hemoperitoneum quantification, comparing its performance against classical segmentation algorithms and visual estimations from trained radiologists. While our results affirm the technical feasibility of HUVAO for this purpose, the observed variations underscore the task's inherent complexity. This emphasizes the limitations of relying solely on HU-based detection, advocating for integration with clinical data. This insight urges exploration of advanced techniques to boost accuracy and elevate patient care standards.