Abstract：
Pericardial effusion can be a sign of significant underlying diease and, in some cases, may lead to death. Post-mortem computed tomography (PMCT) is a well-established tool to assist death investigation processes in the forensic setting. In practice, the scarcity of well-trained radiologists is a challenge in processing raw whole-body PMCT images for pericardial effusion detection. In this work, we propose a Pericardial Effusion Automatic Detection (PEAD) framework to automatically process raw whole-body PMCT images to filter out the irrelevant images with heart organ absent and focus on pericardial effusion detection. In PEAD, the standard convolutional neural network architectures of VGG and ResNet are carefully modified to fit the specific characteristics of PMCT images. The experimental results prove the effectiveness of the proposed framework and modified models. The modified VGG and ResNet models achieved superior detection accuracy than the standard architecture with reduced processing speed.
摘要：
心包积液可能是明显的潜在疾病的征兆，在某些情况下,可能会导致死亡。验尸计算机断层扫描（PMCT）是在法医环境中协助死亡调查过程的公认工具。在实践中,缺乏训练有素的放射科医师是处理原始全身PMCT图像以检测心包积液的一个挑战.在这项工作中,我们提出了一个心包积液自动检测(PEAD)框架来自动处理原始的全身PMCT图像,以滤除心脏器官缺失的无关图像,并专注于心包积液检测.在PEAD,VGG和ResNet的标准卷积神经网络架构经过仔细修改，以适应PMCT图像的特定特征。实验结果证明了所提出的框架和改进模型的有效性。改进的VGG和ResNet模型实现了比标准架构更高的检测精度，处理速度降低。
