익사 사례에서 Postmortem CT 영상을 활용한 U-Net 기반 나비굴 내 액체 자동 분할: 타당성 연구

• Published in: 대한법의학회지, 48(1) pp. 7 - 13
• Main Authors: 허진행, 장선정, 권정화, 고영산, 임상범, 이수경, 서인수, 나주영, 김예지, 윤용수
• Format: Journal Article
• Language: Korean
• Published: 대한법의학회 01.02.2024
• Subjects: 기타의약학
• ISSN: 2383-5702; 2383-5710

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. KCI Citation Count: 0