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Characterisation of organs in a post-mortem computed tomography database using machine learning

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thesis
posted on 03.04.2020 by CARLOS ANDRES PENA SOLORZANO
In this thesis, the effects of a wide range of decedent-presentations on an automated labelling pipeline for analysing post-mortem computed tomography (CT) scans are investigated. This document is organised into three investigations: development of a machine learning (ML) pipeline applied to forensic data; the development of a tool for generation of synthetic trabecular structures; and, the quantification of the effect of artefacts on image quality caused by dense structures in CT scans.

History

Principal supervisor

Matthew Richard Dimmock

Additional supervisor 1

David Albrecht

Year of Award

2020

Department, School or Centre

Medical Imaging and Radiation Sciences

Campus location

Australia

Course

Doctor of Philosophy

Degree Type

DOCTORATE

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