CC BY 4.0 · Journal of Gastrointestinal and Abdominal Radiology
DOI: 10.1055/s-0043-1778672
Original Article

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

Rahul Bhagawati
1   Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, North Guwahati, Assam, India
Suman Hazarika
2   Division of CT, MRI, and Conventional Radiology, Apollo Hospitals Guwahati, Guwahati, Assam, India
Cota Navin Gupta
1   Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, North Guwahati, Assam, India
Souptick Chanda
1   Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, North Guwahati, Assam, India
3   Mehta Family School of Data Science and Artificial Intelligence, Indian Institute of Technology Guwahati, North Guwahati, Assam, India
› Author Affiliations
Funding None.


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.

Ethical Approval Statement

The present study utilized computed tomography (CT) data sourced from the data archive of the hospital. It is important to note that the data used in this study had previously served to diagnose and treat other medical conditions, making the study inherently retrospective.

Publication History

Article published online:
16 January 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (

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