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Potential use of deep learning techniques for postmortem imaging


Dobay, Akos; Ford, Jonathan; Decker, Summer; Ampanozi, Garyfalia; Franckenberg, Sabine; Affolter, Raffael; Sieberth, Till; Ebert, Lars C (2020). Potential use of deep learning techniques for postmortem imaging. Forensic Science, Medicine, and Pathology, 16(4):671-679.

Abstract

The use of postmortem computed tomography in forensic medicine, in addition to conventional autopsy, is now a standard procedure in several countries. However, the large number of cases, the large amount of data, and the lack of postmortem radiology experts have pushed researchers to develop solutions that are able to automate diagnosis by applying deep learning techniques to postmortem computed tomography images. While deep learning techniques require a good understanding of image analysis and mathematical optimization, the goal of this review was to provide to the community of postmortem radiology experts the key concepts needed to assess the potential of such techniques and how they could impact their work.

Abstract

The use of postmortem computed tomography in forensic medicine, in addition to conventional autopsy, is now a standard procedure in several countries. However, the large number of cases, the large amount of data, and the lack of postmortem radiology experts have pushed researchers to develop solutions that are able to automate diagnosis by applying deep learning techniques to postmortem computed tomography images. While deep learning techniques require a good understanding of image analysis and mathematical optimization, the goal of this review was to provide to the community of postmortem radiology experts the key concepts needed to assess the potential of such techniques and how they could impact their work.

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Additional indexing

Item Type:Journal Article, refereed, further contribution
Communities & Collections:04 Faculty of Medicine > Institute of Legal Medicine
Dewey Decimal Classification:570 Life sciences; biology
590 Animals (Zoology)
Scopus Subject Areas:Health Sciences > Pathology and Forensic Medicine
Uncontrolled Keywords:Pathology and Forensic Medicine, General Medicine
Language:English
Date:1 December 2020
Deposited On:18 Dec 2020 13:07
Last Modified:24 Apr 2024 01:46
Publisher:Springer
ISSN:1547-769X
OA Status:Hybrid
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1007/s12024-020-00307-3
PubMed ID:32990926
  • Content: Published Version
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)