Analysis of injured-skin SS-OCT images based on combined attention UNet
Xiyu Zheng, Jingyuan Wu, Qiong Ma, Diantao Luo, Qingyu Cai, Haiyang Sun, Hongxing Kang
TL;DR
This study uses improved UNet models with attention mechanisms to analyze laser-induced skin damage in mice using SS-OCT images, showing strong correlations between laser dose, recovery time, and damage volume.
Contribution
The novel contribution is the development and evaluation of three attention-based UNet models for accurate segmentation and quantitative analysis of laser-induced skin damage in SS-OCT images.
Findings
ParallelAT-UNet achieved a Dice coefficient of 0.9364 and 99.39% accuracy in segmenting skin damage regions.
Laser doses between 44.2 J/cm² and 74.4 J/cm² caused significant changes in skin damage volume, varying with dose and recovery time.
All groups showed healing by 14 days post-treatment, with damage volumes smaller than initial measurements.
Abstract
Optical coherence tomography (OCT) is a noninvasive imaging technique that provides high-resolution images of superficial skin tissues and has become widely used for diagnosing various skin disorders. Assessing laser-induced skin tissue damage is essential for understanding the healing mechanisms and optimizing treatment strategies. However, effectively quantifying skin damage and its correlation with laser dosage and recovery time poses a challenge. In this study, we established a laser-induced skin injury model in mice, utilizing 1 μm–2 μm laser wavelengths. We obtained SS-OCT images of the injury site under different laser doses and recovery times. To enhance image clarity, we applied noise reduction using the BM3D algorithm. We employed an improved UNet network model that incorporates SimAM and PSA modules, forming three attention mechanisms: TandemAT-UNet, ParallelAT-UNet, and…
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Taxonomy
TopicsOptical Coherence Tomography Applications · Ocular and Laser Science Research · Laser Material Processing Techniques