TITLE: HandCT: hands-on computational dataset for X-Ray Computed Tomography and Machine-Learning 

ABSTRACT: Machine-learning methods rely on sufficiently large dataset to learn data
distributions. They are widely used in research in X-Ray Computed Tomography,
from low-dose scan denoising to optimisation of the reconstruction process. The
lack of datasets prevents the scalability of these methods to realistic 3D
problems. We develop a 3D procedural dataset in order to produce samples for
data-driven algorithms. It is made of a meshed model of a left hand and a
script to randomly change its anatomic properties and pose whilst conserving
realistic features. This open-source solution relies on the freeware Blender
and its Python core. Blender handles the modelling, the mesh and the generation
of the hand's pose, whilst Python processes file format conversion from obj
file to matrix and functions to scale and center the volume for further
processing. Dataset availability and quality drives research in
machine-learning. We design a dataset that weighs few megabytes, provides
truthful samples and proposes continuous enhancements using version control. We
anticipate this work to be a starting point for anatomically accurate
procedural datasets. For instance, by adding more internal features and fine
tuning their X-Ray attenuation properties.
Machine-learning methods rely on sufficiently large dataset to learn data
distributions. They are widely used in research in X-Ray Computed Tomography,
from low-dose scan denoising to optimisation of the reconstruction process. The
lack of datasets prevents the scalability of these methods to realistic 3D
problems. We develop a 3D procedural dataset in order to produce samples for
data-driven algorithms. It is made of a meshed model of a left hand and a
script to randomly change its anatomic properties and pose whilst conserving
realistic features. This open-source solution relies on the freeware Blender
and its Python core. Blender handles the modelling, the mesh and the generation
of the hand's pose, whilst Python processes file format conversion from obj
file to matrix and functions to scale and center the volume for further
processing. Dataset availability and quality drives research in
machine-learning. We design a dataset that weighs few megabytes, provides
truthful samples and proposes continuous enhancements using version control. We
anticipate this work to be a starting point for anatomically accurate
procedural datasets. For instance, by adding more internal features and fine
tuning their X-Ray attenuation properties.
HandCT: hands-on computational dataset for X-Ray Computed Tomography and Machine-Learning
http://arxiv.org/abs/2304.14412v1