Abstract
BackgroundStudying structural and functional morphology of small organisms such as monogenean, is difficult due to the lack of visualization in three dimensions. One possible way to resolve this visualization issue is to create digital 3D models which may aid researchers in studying morphology and function of the monogenean. However, the development of 3D models is a tedious procedure as one will have to repeat an entire complicated modelling process for every new target 3D shape using a comprehensive 3D modelling software. This study was designed to develop an alternative 3D modelling approach to build 3D models of monogenean anchors, which can be used to understand these morphological structures in three dimensions. This alternative 3D modelling approach is aimed to avoid repeating the tedious modelling procedure for every single target 3D model from scratch.ResultAn automated 3D modeling pipeline empowered by an Artificial Neural Network (ANN) was developed. This automated 3D modelling pipeline enables automated deformation of a generic 3D model of monogenean anchor into another target 3D anchor. The 3D modelling pipeline empowered by ANN has managed to automate the generation of the 8 target 3D models (representing 8 species: Dactylogyrus primaries, Pellucidhaptor merus, Dactylogyrus falcatus, Dactylogyrus vastator, Dactylogyrus pterocleidus, Dactylogyrus falciunguis, Chauhanellus auriculatum and Chauhanellus caelatus) of monogenean anchor from the respective 2D illustrations input without repeating the tedious modelling procedure.ConclusionsDespite some constraints and limitation, the automated 3D modelling pipeline developed in this study has demonstrated a working idea of application of machine learning approach in a 3D modelling work. This study has not only developed an automated 3D modelling pipeline but also has demonstrated a cross-disciplinary research design that integrates machine learning into a specific domain of study such as 3D modelling of the biological structures.
Highlights
Studying structural and functional morphology of small organisms such as monogenean, is difficult due to the lack of visualization in three dimensions
Despite some constraints and limitation, the automated 3D modelling pipeline developed in this study has demonstrated a working idea of application of machine learning approach in a 3D modelling work
This study has developed an automated 3D modelling pipeline and has demonstrated a cross-disciplinary research design that integrates machine learning into a specific domain of study such as 3D modelling of the biological structures
Summary
Studying structural and functional morphology of small organisms such as monogenean, is difficult due to the lack of visualization in three dimensions. Autodesk 3ds Max requires technical skill to create a decent 3D shape of a target specimen due to the high complexity of the software user interface Another approach would be to construct a new digital 3D model from an existing 3D model via a shape deformation technique [9]. This shape deformation technique obviates the need for repeating a complicated modelling process to construct a new 3D model from scratch One limitation of this technique is that it demands a modeller to manually choose the vertices of a digital generic 3D model and align them with the landmark points of an input 2D image. If this can be automated with minimal human intervention, the entire modelling procedure may become much easier and efficient
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