A 2.5D Deep Learning-Based Method for Drowning Diagnosis Using Post-Mortem Computed Tomography

Yuwen Zeng, Xiaoyong Zhang, Yusuke Kawasumi, Akihito Usui, Kei Ichiji, Masato Funayama, Noriyasu Homma

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1026-1035
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Issue number2
Publication statusPublished - 2023 Feb 1


  • Computed tomography
  • computer-aided diagnosis
  • deep learning
  • drowning
  • explainability


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