A 2.5D Deep Learning-Based Method for Drowning Diagnosis Using Post-Mortem Computed Tomography
A 2.5D Deep Learning-Based Method for Drowning Diagnosis Using Post-Mortem Computed Tomography
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基于 2.5D 深度学习的基于尸检计算机断层扫描的溺水诊断方法
DOI：
10.1109/jbhi.2022.3225416
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发表时间：
2023
影响因子：
7.7
通讯作者：
Homma Noriyasu
中科院分区：
文献类型：
--
作者：
Zeng Yuwen;Zhang Xiaoyong;Kawasumi Yusuke;Usui Akihito;Ichiji Kei;Funayama Masato;Homma Noriyasu
由于溺水的病理生理复杂，法医学专家缺乏放射学知识，即使借助死后多层螺旋CT（MSCT）也难以在尸检中诊断溺水。因此，开发了计算机辅助诊断（CAD）系统来帮助诊断。大多数基于深度学习的CAD系统仅利用2D信息，这适用于胸部X射线图像等2D数据。然而，对于CT等3D数据，也应该考虑3D信息。传统的3D方法在使用3D方法时需要大量的数据和计算成本。在本文中，我们提出了一种2.5D方法，将3D数据转换为2D图像，以训练用于溺水诊断的2D深度学习模型。这种2.5D方法的关键点在于它使用一个子集来表示整个案例，尽可能多地覆盖这个案例，同时避免其他重复信息。为了评估所提出的方法的有效性，使用从东北大学获得的MSCT数据集测试了传统的2D，以前的2.5D和3D深度学习方法。然后，为了提供可解释的诊断结果，采用了一种可视化方法，称为加权类别激活映射，以可视化CT图像中与溺水相关的特征。溺水诊断的结果表明，我们提出的方法相比，其他2D，2.5D和3D的方法取得了最好的性能。视觉评估也表明，我们的方法可以找到相应的溺水的显着区域。
It is challenging to diagnose drowning in autopsy even with the help of post-mortem multi-slice computed tomography (MSCT) due to the complex pathophysiology and the shortage of forensic specialists equipped with radiology knowledge. Therefore, a computer-aided diagnosis (CAD) system was developed to help with diagnosis. Most deep learning-based CAD systems only utilize 2D information, which is proper for 2D data such as chest X-ray images. However, 3D information should also be considered for 3D data like CT. Conventional 3D methods require a huge amount of data and computational cost when using 3D methods. In this article, we proposed a 2.5D method that converts 3D data into 2D images to train 2D deep learning models for drowning diagnosis. The key point of this 2.5D method is that it uses a subset to represent the whole case, covering this case as much as possible while avoiding other repetitive information. To evaluate the effectiveness of the proposed method, conventional 2D, previous 2.5D, and 3D deep learning-based methods were tested using an MSCT dataset obtained from Tohoku university. Then, to provide explainable diagnosis results, a visualization method called Gradient-weighted Class Activation Mapping was employed to visualize features relevant to drowning in CT images. Results on drowning diagnosis showed that our proposed method achieved the best performance compared to other 2D, 2.5D, and 3D methods. The visual assessment also demonstrated that our method could find the saliency regions corresponding to drowning.