A novel method of cadaveric data acquisition from a dissection of the male lower limb using the Perceptron ScanWorks® V5 scanner

Welsh, Elizabeth (2011) A novel method of cadaveric data acquisition from a dissection of the male lower limb using the Perceptron ScanWorks® V5 scanner. MSc(R) thesis, University of Glasgow.

Full text available as:
Download (51MB) | Preview
Printed Thesis Information: https://eleanor.lib.gla.ac.uk/record=b2891400


Introduction: Under the current pressures of an expanding medical curriculum, the time allocated to anatomical training in medical education has been greatly reduced, resulting in an increasing number of doctors qualifying from medical school with an inadequate, and arguably unsafe level of anatomical understanding. Given the limited time now available for cadaveric dissection in medical training, future rectification of these deficits is becoming heavily dependent on supplementation from virtual anatomical training tools. In light of this, many attempts have been made to acquire cadaveric data for the creation of realistic virtual specimens. Until now however, the educational value of these training tools has been heavily scrutinised, with many sharing the view that they are over simplified and anatomically inaccurate.
The main problems associated with the usability of pre-existing datasets arise primarily as a result of the methodology used to acquire their cadaveric data. Projects in this field have previously approached the task of cadaveric data acquisition by creating comprehensive libraries of anatomical cross-sections, from which three-dimensional processing can be technically difficult and not always successful for the reconstruction of fine or complex anatomical structures.

Aim: The aim of this study therefore was to approach cadaveric data acquisition, for the purpose of creating a digital cadaveric specimen, in an unconventional manner, by obtaining three-dimensional data directly from cadaveric tissues with a Perceptron ScanWorksV5 non-contact laser scanner.

Methods: To do this, a carefully planned dissection of the lower limb was performed on a 68 year old male cadaver, and laser scanning of all clinically relevant structures was undertaken at sequential stages throughout. In addition to this, colour and texture information was collected from the cadaveric tissues by high-resolution digital photography.

Results: A comprehensive three-dimensional dataset was acquired from all clinically relevant anatomy of the lower limb as a result of the methodology used in this study. Data was obtained at extremely high point to point resolutions, with a measurement accuracy of 24μm, 2σ.

Discussion: By obtaining cadaveric data in this way, the problems associated with the three-dimensional processing of conventional cross-sectional data, such as image segmentation, are largely overcome and fine anatomical details can be accurately documented with high precision. This data can be processed further to create an accurate and realistic virtual reconstruction of the lower limb for both three-dimensional anatomical training and surgical rehearsal in the future.

Conclusion: Whilst the value of cross-sectional datasets in their own right should not be disputed, the methodology used for cadaveric data acquisition in this study has proved a very successful in collecting three-dimensional data directly form the specimen, and could be used to acquire detailed datasets for the reconstruction of other complex body regions for virtual anatomical training in the future.

Item Type: Thesis (MSc(R))
Qualification Level: Masters
Keywords: Virtual anatomy, cadaveric data acquisition, dissection, laser scanning, 3D reconstruction
Subjects: Q Science > QM Human anatomy
Colleges/Schools: College of Medical Veterinary and Life Sciences > Institute of Neuroscience and Psychology
Supervisor's Name: Rea, Dr. Paul
Date of Award: 2011
Depositing User: Miss Elizabeth Welsh
Unique ID: glathesis:2011-2824
Copyright: Copyright of this thesis is held by the author.
Date Deposited: 29 Nov 2011
Last Modified: 10 Dec 2012 14:00
URI: http://theses.gla.ac.uk/id/eprint/2824

Actions (login required)

View Item View Item


Downloads per month over past year