# Automated Traumatic bleeding Detection in Whole-Body CT Using 3D Object Detection Model
## 1 Introduction  
Traumatic injury is a leading cause of mortality worldwide, particularly among young adults [1, 2]. Common causes include traffic accidents, falls, and acts of violence, which often require immediate medical intervention, such as resuscitation or surgical procedures. Traumatic bleeding, caused by vascular rupture, can lead to blood extravasation into tissues, organs, or body cavities and represents a critical, potentially fatal complication. Uncontrolled bleeding is the leading cause of preventable trauma-related deaths, accounting for approximately 40% of such fatalities [3].  
CAD software has seen active advancements in the detection of various abnormalities [6, 7], and several applications have been developed for trauma CT images, including those for detecting fractures [8, 9], critical findings in head CT images [10], bleeding in head CT images [11, 12, 13, 14, 15], bleeding in WBCT images [16, 17, 18], and abdominal injuries, including extravasation [19]. Although bleeding detection has been extensively studied in head CT images, studies targeting other body regions remain limited. For example, [PERSON] et al. [19] addressed various types of abdominal injuries, including extravasation, using a slice-by-slice classification approach based on two-dimensional (2D) segmentation and 2.5D classification. While [PERSON] et al. employed a slice-wise classification approach, our previous studies explored a three-dimensional (3D) segmentation model that directly identifies bleeding regions in WBCT images [16, 17, 18]. Our latest approach [18] integrates 3D U-Net#-based bleeding detection with false positive (FP) reduction based on multi-organ segmentation, yielding 76.0 FPs per case at a sensitivity of 70.0%, with a processing time of approximately 6 min. Although this method shows promise, reducing false positives and accelerating the process would likely make it more practical for clinical use.  
Given the limitations of our previous segmentation-based method, we adopted a 3D object detection framework. This approach was inspired by the promising results reported by [PERSON] et al. [20], who demonstrated that 3D object detection models can outperform segmentation-based methods in terms of both detection performance and processing speed. While [PERSON] et al. [20] applied 3D object detection to detect cerebral aneurysms in head magnetic resonance angiography images, our study is the first to apply this approach to traumatic bleeding detection in WBCT. Unlike cerebral aneurysms, bleeding regions exhibit wide variability in shape and size. Therefore, instead of assuming spherical lesion shapes as in previous aneurysm detection studies, we adopted a flexible cuboid representation using 3D bounding boxes to better accommodate irregular bleeding patterns. Building on this insight, we propose a novel automated method for detecting traumatic bleeding in WBCT images, based on a 3D object detection model inspired by [20] and enhanced with an atrous spatial pyramid pooling (ASPP) module [21]. The proposed CAD software was evaluated using trauma WBCT images collected from multiple institutions.