The significance of evaluating sphenoid sinus fluid by postmortem computed tomography in cases of drowning

Heo, Jin-Haeng; Yoon, Yongsu; Seo, In-Soo; Lim, Sang-Beom; Ko, Young San; Lee, Sookyoung; Jang, Seon Jung

doi:10.1016/j.jflm.2023.102551

Journal of Forensic and Legal Medicine

2023

DOI: 10.1016/j.jflm.2023.102551

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The significance of evaluating sphenoid sinus fluid by postmortem computed tomography in cases of drowning

Jin-Haeng Heo

Yongsu Yoon

In-Soo Seo

et al.

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Cited by 2 publications

References 19 publications

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Enhancing CT Image Quality Using Conditional Generative Adversarial Networks for Applying Post-mortem Computed Tomography in Forensic Pathology: A Phantom Study

Yoon,

Heo,

Kim

et al. 2023

J. Radiol. Sci. Technol.

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Enhancing CT Image Quality Using Conditional Generative Adversarial Networks for Applying Post-mortem Computed Tomography in Forensic Pathology: A Phantom Study

Yoon,

Heo,

Kim

et al. 2023

J. Radiol. Sci. Technol.

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U-Net-Based Automatic Segmentation of Sphenoid Sinus Fluid in Drowning Cases Using Postmortem CT Images: A Feasibility Study

Heo,

Jang,

Kwon

et al. 2024

Korean J Leg Med

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Detecting sphenoid sinus fluid (SSF) is an additional finding in autopsies for diagnosing drowning. SSF can provide additional forensic evidence through laboratory tests such as diatom and electrolyte analyses. If drowning is suspected, accurately assessing the presence and volume of SSF during an autopsy is crucial. Utilizing postmortem computed tomography (PMCT) images could aid in accurately sampling SSF. Accurately segmenting the region of interest is essential for volume analysis using computed tomography images. However, manual segmentation techniques are labor-intensive and time-consuming, and their success depends on the experience of the observer. Therefore, this study aimed to develop a U-Net–based deep learning model for the automatic segmentation of SSF in drowning cases using PMCT images and to evaluate the performance of the model. We retrospectively reviewed 34 drowning cases in which both PMCT scans and forensic autopsies were performed at our institution. The U-Net architecture of deep learning was used for automatic segmentation. The proposed model achieved the Dice similarity coefficient (DSC) and Intersection over Union (IoU) of a maximum of 95.85% and 92.03%, a minimum of 0% and 0%, and an average of 77.15% and 67.18%, respectively. Although the average DSC and IoU did not show high similarity, this study showed that PMCT images can be used for automatic segmentation of SSF in drowning cases, which could improve the performance with sufficient dataset acquisition and further model training.

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The significance of evaluating sphenoid sinus fluid by postmortem computed tomography in cases of drowning

Cited by 2 publications

References 19 publications

Enhancing CT Image Quality Using Conditional Generative Adversarial Networks for Applying Post-mortem Computed Tomography in Forensic Pathology: A Phantom Study

Enhancing CT Image Quality Using Conditional Generative Adversarial Networks for Applying Post-mortem Computed Tomography in Forensic Pathology: A Phantom Study

U-Net-Based Automatic Segmentation of Sphenoid Sinus Fluid in Drowning Cases Using Postmortem CT Images: A Feasibility Study

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