Fully automatic CT-histogram-based fat estimation in dead bodies
Post-mortem body cooling is the foundation of temperature-based death time estimations (TDE) in homicide cases. Forensic science generally provides two types of p.m. body cooling models, the phenomenological and the physical models. Since both of them have to implement important individual parameters like the quantity of abdominal fat explicitly or implicitly, a more exact quantification and localization of abdominal fat is a desideratum in TDE. Particularly for the physical models, a better knowledge of the abdominal fat distribution could lead to relevant improvements in TDEs. Modern imaging methods in medicine like computed tomography (CT) are opening up the possibility to register the quantity and spatial distribution of body fat in individual cases with unprecedented precision. Since a CT-scan of an individual’s abdominal region can comprise 1000 slices as an order of magnitude, it is evident that their evaluation for body fat quantification and localization needs fully automated algorithms. The paper at hand describes the development and validation of such an algorithm called “CT-histogram-based fat estimation and quasi-segmentation” (CFES). The approach can be characterized as a weighted least squares method dealing with the gray value histogram of single CT-slices only. It does not require any anatomical a priori information nor does it perform time-consuming feature detection on the CT-images. The processing result consists in numbers quantifying the amount of abdominal body fat and of muscle-, organ-, and connective tissue. As a by-product, CFES generates a quasi-segmentation of the slices processed differentiating fat from muscle-, organ-, and connective tissue. The tool is validated on synthetic data and on CT-data of a special phantom. It was also applied on a CT-scan of a dead body, where it produced anatomically plausible results.
KeywordsBody fat quantification Body fat quasi-segmentation Computed tomography—scans Weighted least squares estimation on gray value histogram Temperature-based death time estimation
We gratefully acknowledge the technical CT aid of Mrs. Antje Kubin as MTRA in IDIR II.
Compliance with ethical standard
Conflict of interest
The authors declare that they have no conflict of interest.
The study was reviewed and approved by the ethics committee of the University Hospital Jena. According to the ethics committee’s statement, written consents of the kinship for the abdominal CT-slices shown in the article were not needed since the bodies were confiscated by the local prosecution who directed the CT-scans for investigations. Moreover, the representations of the slices are totally anonymized.
- 1.Marshall TK, Hoare FE (1962) Estimating the time of death—the rectal cooling after death and its mathematical expression. J Forensic Sci 7:56–81Google Scholar
- 2.Marshall TK, Hoare FE (1962) Estimating the time of death—the use of the cooling formula in the study of postmortem cooling. J Forensic Sci 7:189–210Google Scholar
- 3.Marshall TK, Hoare FE (1962) Estimating the time of death—the use of body temperature in estimating the time of death. J Forensic Sci 7:211–221Google Scholar
- 8.Schenkl S, Muggenthaler H, Hubig M, Erdmann B, Weiser M, Zachow S, Heinrich A, Güttler FV, Teichgräber U, Mall G (2017) Automatic CT-based finite element model generation for temperature-based death time estimation: feasibility study and sensitivity analysis. Int J Legal Med 131:699–712. https://doi.org/10.1007/s00414-016-1523-0 CrossRefPubMedGoogle Scholar
- 9.Aparicio HJ, Himali JJ, Beiser AS et al (2017) Overweight, obesity, and survival after stroke in the Framingham heart study. J Am Heart Assoc 6. https://doi.org/10.1161/jaha.116.004721
- 11.Hasse J (1995) Accuracy of subcutaneous fat measurement: comparison of skinfold calipers, ultrasound, and computed tomography. Orphanidou C, McCargar, Birmingham CL, et al. University of British Columbia and St. Paul's Hospital, Vancouver. Nutrition in Clinical Practice 10: 161-162. https://doi.org/10.1177/088453369501000414
- 13.Hofer M (2010) CT-Kursbuch. Didamed-Verlag DüsseldorfGoogle Scholar
- 14.Press WH, Teukolsky SA, Vetterling WT, Flannery BP (1992) Numerical recipes in C: the art of scientific computing, 2nd edn. Press Syndicate of the University of Cambridge, New YorkGoogle Scholar
- 21.Allen P, Branscheid W, Dobrowolski A, Horn P, Romvari R (2004) Schlachtkörperwertbestimmung beim Schwein – Röntgen-Computertomographie als mögliche Referenzmethode. Fleischwirtschaft 84(3):109–112Google Scholar
- 25.Pednekar A, Bandekar AN, Kakadiaris IA, Naghavi M (2005) Automatic segmentation of abdominal fat from CT data. Proceedings of the Seventh IEEE Workshop on Applications of Computer Vision (WACV/MOTION’05), 2005 WACV/MOTIONS ’05 Volume 1 Seventh IEEE: pp. 308–315Google Scholar
- 30.Kanaly CW, Ding D, Mehta AI, Waller AF, Crocker I, Desjardins A, Reardon DA, Friedman AH, Bigner DD, Sampson JH (2011) A novel method for volumetric MRI response assessment of enhancing brain Tumors. PLoS ONE 6(1):e16031. https://doi.org/10.1371/journal.pone.0016031 CrossRefPubMedPubMedCentralGoogle Scholar
- 31.Subramaniam K, Hoffman EA, Tawhai MH (2012) Engineering in medicine and biology society (EMBC), 2012 Annual International Conference of the IEEE San Diego, California USA, 28 August - 1 September, 4072-4089. https://doi.org/10.1109/EMBC.2012.6345869
- 33.Kullberg J, Hedström A, Brandberg J, Strand R, Johansson L, Bergström G, Ahlström H (2017) Automated analysis of liver fat, muscle and adipose tissue distribution from CT suitable for large-scale studies. Sci Rep 7:10425. https://doi.org/10.1038/s41598-017-08925-8 CrossRefPubMedPubMedCentralGoogle Scholar
- 35.Shahzada R, Bos D, Metz C, Rossi A, van der Lugt A, Klein S, Witteman J, de Feyter P, Niessen W, van Vliet L, van Walsum T (2013) Automatic quantification of epicardial fat volume on non-enhanced cardiac CT scans using a multi-atlas segmentation approach. Med Phys 40(9):091910. https://doi.org/10.1118/1.4817577 CrossRefGoogle Scholar