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Related Concept Videos

Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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AI-based organ weight estimation from postmortem computed tomography.

Marc Windgassen1, Andreas Heinrich2

  • 1Institute of Forensic Medicine, Jena University Hospital, Friedrich Schiller University, Am Klinikum 1, 07747, Jena, Germany.

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|July 13, 2026
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Summary

Artificial Intelligence (AI) and postmortem computed tomography (CT) enable accurate, non-invasive organ weight estimation. This method supports forensic investigations by providing reliable organ measurements from CT scans.

Keywords:
Artificial intelligenceBody compositionBody weights and measuresComputer-assisted image processingForensic medicineMeSHX-ray computed tomography

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Area of Science:

  • Forensic Radiology
  • Medical Imaging Analysis
  • Artificial Intelligence in Medicine

Background:

  • Accurate organ weight determination is crucial in forensic autopsies.
  • Traditional autopsy methods are invasive and time-consuming.
  • Postmortem computed tomography (CT) offers a non-invasive imaging approach.

Purpose of the Study:

  • To evaluate the accuracy of AI-based automated organ segmentation for weight estimation using postmortem CT.
  • To compare CT-derived organ weights with conventional autopsy measurements.
  • To assess the reliability of this technique for forensic applications.

Main Methods:

  • 100 postmortem CT examinations were analyzed.
  • Artificial Intelligence (AI) algorithms performed automated segmentation of brain, heart, liver, kidneys, and spleen.
  • Organ weights were calculated using voxel volumes and Hounsfield unit-based tissue densities from CT data.
  • Two model resolutions (1.5 mm and 3 mm) were evaluated.

Main Results:

  • Mean absolute differences between CT-derived and autopsy-derived organ weights ranged from 70.76 ± 83.07 g (1.5 mm model) to 77.43 ± 90.60 g (3 mm model).
  • Agreement was highest for the brain and liver, followed by kidneys and spleen; the heart showed the largest deviations.
  • Segmentation failures were infrequent, primarily linked to decomposition or trauma.

Conclusions:

  • AI-based segmentation in postmortem CT provides reliable and largely non-invasive organ weight estimation.
  • This technique can significantly support forensic investigations and potentially aid in personal identification using antemortem imaging data.
  • Automated organ weight determination via CT is a promising advancement in forensic radiology.