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:
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 ScanWorksV5 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.
Actions (login required)