An algorithm for automatically generating gas, bone and foreign body visualizations from postmortem computed tomography data.
Lars Christian EbertDilan SeckinerTill SieberthMichael J ThaliSabine FranckenbergPublished in: Forensic science, medicine, and pathology (2021)
Post mortem computed tomography (PMCT) can aid in localizing foreign bodies, bone fractures, and gas accumulations. The visualization of these findings play an important role in the communication of radiological findings. In this article, we present an algorithm for automated visualization of gas distributions on PMCT image data of the thorax and abdomen. The algorithm uses a combination of region growing segmentation and layering of different visualization methods to automatically generate overview images that depict radiopaque foreign bodies, bones and gas distributions in one image. The presented method was tested on 955 PMCT scans of the thorax and abdomen. The algorithm managed to generate useful images for all cases, visualizing foreign bodies as well as gas distribution. The most interesting cases are presented in this article. While this type of visualization cannot replace a real radiological analysis of the image data, it can provide a quick overview for briefings and image reports.
Keyphrases
- deep learning
- computed tomography
- convolutional neural network
- artificial intelligence
- room temperature
- machine learning
- big data
- electronic health record
- positron emission tomography
- carbon dioxide
- bone mineral density
- electron microscopy
- image quality
- body composition
- data analysis
- living cells
- bone loss
- drug induced
- single molecule
- adverse drug