Edge detector‐based automatic segmentation of the skin layers and application to moisturization in high‐resolution 3 Tesla magnetic resonance imaging

Abstract

Introduction Previous studies have demonstrated the feasibility to explore moisturization with quantification imaging based on T2 mapping. The aim of this study was to describe and validate the first robust automated method to segment the first layers of the skin. Materials and Methods Data were picked from a previous study that included 35 healthy subjects who underwent a 3T MRI (multi spin echo calculation T2‐weighted sequence) with a microscopic coil on the left heel before and one hour after moisturization. The automatic algorithm was composed of the T2 map generation, a Canny filter, a selection of boundaries, and a local regression to delimitate stratum corneum, epidermis, and dermis. An automated affine registration was applied between the exams before and after moisturization. Results The failure rate of the algorithm was below 5%. Mean computation time was 139.12s. There was a significant and strong correlation between the automatic measurements and the manual ones for the T2 values (ρ: 0.905, P < 0.001) and for the thickness measurements (ρ: 0.8663; P < 0.001). For registration, mean of the Dice index was 0.64 [0.47; 0.80] and of the Hausdorff distance was 0.29 mm 95% CI: [0.28; 0.30]. Conclusion The proposed automatic method to study the first skin layers in 3T MRI using micro‐coils was robust and described T2 values and thickness measurements with a strong correlation to manual measurements. The use of an automated affine registration could also permit the generation of a mapping for a visual assessment of moisturization.

Use of artificial intelligence in imaging in rheumatology – current status and future perspectives

“…These methods employ for example ultrasound, MRI or optical coherence tomography (OCT) to determine skin thickness and other characteristics. AI-based image analysis methods have been introduced, to fully automatically measure skin thickness40 and other (epi)dermal characteristics41 from ultrasound images, and MRI42 and OCT 43. These automated imaging biomarkers, however, still need to be evaluated because the relatively new imaging techniques require further optimisation and standardisation, and have not yet been applied in SSc.…”