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A systematic review of automated segmentation of 3D computed‐tomography scans for volumetric body composition analysis by Dinh Van Chi Mai; Ioanna Drami; Edward T. Pring; Laura E. Gould; Phillip Lung; Karteek Popuri; Vincent Chow; Mirza F. Beg; Thanos Athanasiou; John T. Jenkins; the BiCyCLE Research Group is a Medicine article available to read on EtoBox.
What is A systematic review of automated segmentation of 3D computed‐tomography scans for volumetric body composition analysis about?
## Abstract Automated computed tomography (CT) scan segmentation (labelling of pixels according to tissue type) is now possible. This technique is being adapted to achieve three‐dimensional (3D) segmentation of CT scans, opposed to single L3‐slice alone. This systematic review evaluates feasibility and accuracy of automated segmentation of 3D CT scans for volumetric body composition (BC) analysis, as well as current limitations and pitfalls clinicians and researchers should be aware of. OVID Medline, Embase and grey literature databases up to October 2021 were searched. Original studies investigating automated skeletal muscle, visceral and subcutaneous AT segmentation from CT were included. Seven of the 92 studies met inclusion criteria. Variation existed in expertise and numbers of humans performing ground‐truth segmentations used to train algorithms. There was heterogeneity in patient characteristics, pathology and CT phases that segmentation algorithms were developed upon. Reporting of anatomical CT coverage varied, with confusing terminology. Six studies covered volumetric regional slabs rather than the whole body. One study stated the use of whole‐body CT, but it was not clear
Who reads A systematic review of automated segmentation of 3D computed‐tomography scans for volumetric body composition analysis?
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- Author
- Dinh Van Chi Mai; Ioanna Drami; Edward T. Pring; Laura E. Gould; Phillip Lung; Karteek Popuri; Vincent Chow; Mirza F. Beg; Thanos Athanasiou; John T. Jenkins; the BiCyCLE Research Group
- Publisher
- Wiley
- Published
- 2023
- Language
- EN
- Field
- Medicine (Health Sciences)