INNODERM`s newest publication in the journal Medical Physics presents a method based on machine learning that enables, for the first time, automated identification of skin layers in ultra-broadband raster-scan optoacoustic mesoscopy (UWB-RSOM) data.
In this paper, the authors present the identification of morphological characteristics of skin lesions is of vital importance in diagnosing diseases with dermatological manifestations. This task is often performed manually or in an automated way based on intensity level. Recently, ultra-broadband raster-scan optoacoustic mesoscopy (UWB-RSOM) was developed to offer unique cross-sectional optical imaging of the skin. A new method (SkinSeg) based on machine learning is proposed here to enable, for the first time, automated identification of skin layers in UWB-RSOM data.
Moustakidis, S., Omar, M., Aguirre, J., Mohajerani, P. and Ntziachristos, V. (2019), Fully automated identification of skin morphology in raster-scan optoacoustic mesoscopy using artificial intelligence. Med. Phys., 46: 4046-4056. https://doi.org/10.1002/mp.13725