Analysis of injured-skin SS-OCT images based on combined attention UNet.
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 an...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0324327 |
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| author | Xiyu Zheng Jingyuan Wu Qiong Ma Diantao Luo Qingyu Cai Haiyang Sun Hongxing Kang |
| author_facet | Xiyu Zheng Jingyuan Wu Qiong Ma Diantao Luo Qingyu Cai Haiyang Sun Hongxing Kang |
| author_sort | Xiyu Zheng |
| collection | DOAJ |
| description | 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 [Formula: see text]m-2 [Formula: see text]m 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 NestedAT-UNet. These models were used to segment the damaged skin regions, followed by a 3D reconstruction method to quantitatively evaluate the volume of skin damage while analyzing changes about laser dose and recovery time.The BM3D algorithm effectively suppressed high-noise components, significantly improving image clarity. Among the three models, ParallelAT-UNet exhibited the best segmentation performance, achieving a Dice coefficient of 0.9364, mean Pixel Accuracy (mPA) of 92.67%, mean Intersection over Union (mIoU) of 96.31%, and an accuracy of 99.39%. Quantitative analysis revealed that laser doses between [Formula: see text] and [Formula: see text] caused minimal changes in skin damage volume, while doses ranging from [Formula: see text] to [Formula: see text] resulted in significant changes, which varied according to both the dose and recovery time. All groups showed signs of healing by 14 days post-laser treatment, with damage volumes smaller than the initial values. This study presents an efficient and reliable method for the quantitative assessment of laser-induced skin damage using OCT imaging. The findings demonstrate a strong relationship between laser dosage, recovery time, and skin damage, highlighting potential applications for noninvasive diagnosis and treatment monitoring using OCT. |
| format | Article |
| id | doaj-art-08833fcd9bce40a3aebd519e813a534e |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-08833fcd9bce40a3aebd519e813a534e2025-08-20T02:40:28ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01207e032432710.1371/journal.pone.0324327Analysis of injured-skin SS-OCT images based on combined attention UNet.Xiyu ZhengJingyuan WuQiong MaDiantao LuoQingyu CaiHaiyang SunHongxing KangOptical 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 [Formula: see text]m-2 [Formula: see text]m 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 NestedAT-UNet. These models were used to segment the damaged skin regions, followed by a 3D reconstruction method to quantitatively evaluate the volume of skin damage while analyzing changes about laser dose and recovery time.The BM3D algorithm effectively suppressed high-noise components, significantly improving image clarity. Among the three models, ParallelAT-UNet exhibited the best segmentation performance, achieving a Dice coefficient of 0.9364, mean Pixel Accuracy (mPA) of 92.67%, mean Intersection over Union (mIoU) of 96.31%, and an accuracy of 99.39%. Quantitative analysis revealed that laser doses between [Formula: see text] and [Formula: see text] caused minimal changes in skin damage volume, while doses ranging from [Formula: see text] to [Formula: see text] resulted in significant changes, which varied according to both the dose and recovery time. All groups showed signs of healing by 14 days post-laser treatment, with damage volumes smaller than the initial values. This study presents an efficient and reliable method for the quantitative assessment of laser-induced skin damage using OCT imaging. The findings demonstrate a strong relationship between laser dosage, recovery time, and skin damage, highlighting potential applications for noninvasive diagnosis and treatment monitoring using OCT.https://doi.org/10.1371/journal.pone.0324327 |
| spellingShingle | Xiyu Zheng Jingyuan Wu Qiong Ma Diantao Luo Qingyu Cai Haiyang Sun Hongxing Kang Analysis of injured-skin SS-OCT images based on combined attention UNet. PLoS ONE |
| title | Analysis of injured-skin SS-OCT images based on combined attention UNet. |
| title_full | Analysis of injured-skin SS-OCT images based on combined attention UNet. |
| title_fullStr | Analysis of injured-skin SS-OCT images based on combined attention UNet. |
| title_full_unstemmed | Analysis of injured-skin SS-OCT images based on combined attention UNet. |
| title_short | Analysis of injured-skin SS-OCT images based on combined attention UNet. |
| title_sort | analysis of injured skin ss oct images based on combined attention unet |
| url | https://doi.org/10.1371/journal.pone.0324327 |
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