  Advanced artificial intelligence and machine learning have great potential to
redefine how skin lesions are detected, mapped, tracked and documented. Here,
We propose a 3D whole-body imaging system known as 3DSkin-mapper to enable
automated detection, evaluation and mapping of skin lesions. A modular camera
rig arranged in a cylindrical configuration was designed to automatically
capture images of the entire skin surface of a subject synchronously from
multiple angles. Based on the images, we developed algorithms for 3D model
reconstruction, data processing and skin lesion detection and tracking based on
deep convolutional neural networks. We also introduced a customised,
user-friendly, and adaptable interface that enables individuals to
interactively visualise, manipulate, and annotate the images. The proposed
system is developed for skin lesion screening, the focus of this paper is to
introduce the system instead of clinical study. Using synthetic and real images
we demonstrate the effectiveness of the proposed system by providing multiple
views of a target skin lesion, enabling further 3D geometry analysis and
longitudinal tracking. It takes only a few seconds to capture the entire skin
surface, and about half an hour to process and analyse the images. Our
experiments show that the proposed system allow fast and easy whole body 3D
imaging. It can be used by dermatological clinics to conduct skin screening,
detect and track skin lesions over time, identify suspicious lesions, and
document pigmented lesions. The system can potentially save clinicians time and
effort significantly. The 3D imaging and analysis has the potential to change
the paradigm of whole body photography with many applications in skin diseases,
including inflammatory and pigmentary disorders.
