A Novel Method of Anatomical Data Acquisition Using the Perceptron ScanWorks V5 Scanner

A drastic reduction in the time available for cadaveric dissection and anatomy teaching in medical and surgical education has increased the requirement to supplement learning with the use of virtual gross anatomy training tools. In light of this, a number of known studies have approached the task of sourcing anatomical data from cadaveric material for end us in creating 3D reconstructions of the human body by producing vast image libraries of anatomical cross sections. However, the processing involved in the conversion of cross sectional images to reconstructions in 3D elicits a number of problems in creating an accurate and adequately detailed end product, suitable for educational. In this paperwe have employed a unique approach in a pilot study acquire anatomical data for end-use in 3D anatomical reconstruction by using topographical 3D laser scanning and high-resolution digital photography of all clinically relevant structures from the lower limb of a male cadaveric specimen. As a result a comprehensive high-resolution dataset, comprising 3D laser scanned data and corresponding colour photography was obtained from all clinically relevant gross anatomical structures associated with the male lower limb. This unique dataset allows a very unique and novel way to capture anatomical data and saves on the laborious processing of image segmentation common to conventional image acquisition used clinically, like CT and MRI scans. From this, it provides a dataset which can then be used across a number of commercial products dependent on the end-users requirements for development of computer training packages in medical and surgical rehearsal

A Novel Method of Anatomical Data Acquisition Using the Perceptron ScanWorks V5 Scanner

A drastic reduction in the time available for cadaveric dissection and anatomy teaching in medical and surgical education has increased the requirement to supplement learning with the use of virtual gross anatomy training tools. In light of this, a number of known studies have approached the task of sourcing anatomical data from cadaveric material for end us in creating 3D reconstructions of the human body by producing vast image libraries of anatomical cross sections. However, the processing involved in the conversion of cross sectional images to reconstructions in 3D elicits a number of problems in creating an accurate and adequately detailed end product, suitable for educational. In this paperwe have employed a unique approach in a pilot study acquire anatomical data for end-use in 3D anatomical reconstruction by using topographical 3D laser scanning and high-resolution digital photography of all clinically relevant structures from the lower limb of a male cadaveric specimen. As a result a comprehensive high-resolution dataset, comprising 3D laser scanned data and corresponding colour photography was obtained from all clinically relevant gross anatomical structures associated with the male lower limb. This unique dataset allows a very unique and novel way to capture anatomical data and saves on the laborious processing of image segmentation common to conventional image acquisition used clinically, like CT and MRI scans. From this, it provides a dataset which can then be used across a number of commercial products dependent on the end-users requirements for development of computer training packages in medical and surgical rehearsal

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

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

Instruction with 3D Computer Generated Anatomy

Research objectives. 1) To create an original and useful software application; 2) to investigate the utility of dyna-linking for teaching upper limb anatomy. Dyna-linking is an arrangement whereby interaction with one representation automatically drives the behaviour of another representation. Method. An iterative user-centred software development methodology was used to build, test and refine successive prototypes of an upper limb software tutorial. A randomised trial then tested the null hypothesis: There will be no significant difference in learning outcomes between participants using dyna-linked 2D and 3D representations of the upper limb and those using non dyna-linked representations. Data was analysed in SPSS using factorial analysis of variance (ANOVA). Results and analysis. The study failed to reject the null hypothesis as there was no signi cant di fference between experimental conditions. Post-hoc analysis revealed that participants with low prior knowledge performed significantly better (p = 0.036) without dyna-linking (mean gain = 7.45) than with dyna-linking (mean gain = 4.58). Participants with high prior knowledge performed equally well with or without dyna-linking. These findings reveal an aptitude by treatment interaction (ATI) whereby the effectiveness of dyna-linking varies according to learner ability. On average, participants using the non dyna-linked system spent 3 minutes and 4 seconds longer studying the tutorial. Participants using the non dyna-linked system clicked 30% more on the representations. Dyna-linking had a high perceived value in questionnaire surveys (n=48) and a focus group (n=7). Conclusion. Dyna-linking has a high perceived value but may actually over-automate learning by prematurely giving novice learners a fully worked solution. Further research is required to confirm if this finding is repeated in other domains, with different learners and more sophisticated implementations of dyna-linking