Abstract
Modern data-driven machine learning research that enables revolutionary advances in image analysis has now become a critical tool to redefine how skin lesions are documented, mapped, and tracked. We propose a 3D whole body imaging prototype to enable rapid evaluation and mapping of skin lesions. A modular camera rig arranged in a cylindrical configuration is designed to automatically capture synchronised images from multiple angles for entire body scanning. We develop algorithms for 3D body image reconstruction, data processing and skin lesion detection based on deep convolutional neural networks. We also propose a customised, intuitive and flexible interface that allows the user to interact and collaborate with the machine to understand the data. The hybrid of the human and computer is represented by the analysis of 2D lesion detection, 3D mapping and data management. The experimental results using synthetic and real images demonstrate the effectiveness of the proposed solution by providing multiple views of the target skin lesion, enabling further 3D geometry analysis. Skin lesions are identified as outliers which deserve more attention from a skin cancer physician. Our detector identifies lesions at a comparable performance level as a physician. The proposed 3D whole body imaging system can be used by dermatological clinics, allowing for fast documentation of lesions, quick and accurate analysis of the entire body to detect suspicious lesions. Because of its fast examination, the method might be used for screening or epidemiological investigations. 3D data analysis has the potential to change the paradigm of total-body photography with many applications in skin diseases, including inflammatory and pigmentary disorders.
Abstract (translated)
URL
https://arxiv.org/abs/2205.07085
