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Wu J, Ma Q, Zhou X, Wei Y, Liu Z, Kang H. Segmentation and quantitative analysis of optical coherence tomography (OCT) images of laser burned skin based on deep learning. Biomed Phys Eng Express 2024; 10:045026. [PMID: 38718764 DOI: 10.1088/2057-1976/ad488f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 05/08/2024] [Indexed: 05/22/2024]
Abstract
Evaluation of skin recovery is an important step in the treatment of burns. However, conventional methods only observe the surface of the skin and cannot quantify the injury volume. Optical coherence tomography (OCT) is a non-invasive, non-contact, real-time technique. Swept source OCT uses near infrared light and analyzes the intensity of light echo at different depths to generate images from optical interference signals. To quantify the dynamic recovery of skin burns over time, laser induced skin burns in mice were evaluated using deep learning of Swept source OCT images. A laser-induced mouse skin thermal injury model was established in thirty Kunming mice, and OCT images of normal and burned areas of mouse skin were acquired at day 0, day 1, day 3, day 7, and day 14 after laser irradiation. This resulted in 7000 normal and 1400 burn B-scan images which were divided into training, validation, and test sets at 8:1.5:0.5 ratio for the normal data and 8:1:1 for the burn data. Normal images were manually annotated, and the deep learning U-Net model (verified with PSPNe and HRNet models) was used to segment the skin into three layers: the dermal epidermal layer, subcutaneous fat layer, and muscle layer. For the burn images, the models were trained to segment just the damaged area. Three-dimensional reconstruction technology was then used to reconstruct the damaged tissue and calculate the damaged tissue volume. The average IoU value and f-score of the normal tissue layer U-Net segmentation model were 0.876 and 0.934 respectively. The IoU value of the burn area segmentation model reached 0.907 and f-score value reached 0.951. Compared with manual labeling, the U-Net model was faster with higher accuracy for skin stratification. OCT and U-Net segmentation can provide rapid and accurate analysis of tissue changes and clinical guidance in the treatment of burns.
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Affiliation(s)
- Jingyuan Wu
- Beijing Institute of Radiation Medicine, Beijing 100850, People's Republic of China
- College of Life Sciences, Hebei University, Baoding, Hebei 071002, People's Republic of China
| | - Qiong Ma
- Beijing Institute of Radiation Medicine, Beijing 100850, People's Republic of China
| | - Xun Zhou
- Beijing Institute of Radiation Medicine, Beijing 100850, People's Republic of China
| | - Yu Wei
- Beijing Institute of Radiation Medicine, Beijing 100850, People's Republic of China
- College of Life Sciences, Hebei University, Baoding, Hebei 071002, People's Republic of China
| | - Zhibo Liu
- Beijing Institute of Radiation Medicine, Beijing 100850, People's Republic of China
| | - Hongxiang Kang
- Beijing Institute of Radiation Medicine, Beijing 100850, People's Republic of China
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Wang C, Ma Q, Wei Y, Liu Q, Wang Y, Xu C, Li C, Cai Q, Sun H, Tang X, Kang H. Deep learning automatically assesses 2-µm laser-induced skin damage OCT images. Lasers Med Sci 2024; 39:106. [PMID: 38634947 DOI: 10.1007/s10103-024-04053-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 04/08/2024] [Indexed: 04/19/2024]
Abstract
The present study proposed a noninvasive, automated, in vivo assessment method based on optical coherence tomography (OCT) and deep learning techniques to qualitatively and quantitatively analyze the biological effects of 2-µm laser-induced skin damage at different irradiation doses. Different doses of 2-µm laser irradiation established a mouse skin damage model, after which the skin-damaged tissues were imaged non-invasively in vivo using OCT. The acquired images were preprocessed to construct the dataset required for deep learning. The deep learning models used were U-Net, DeepLabV3+, PSP-Net, and HR-Net, and the trained models were used to segment the damage images and further quantify the damage volume of mouse skin under different irradiation doses. The comparison of the qualitative and quantitative results of the four network models showed that HR-Net had the best performance, the highest agreement between the segmentation results and real values, and the smallest error in the quantitative assessment of the damage volume. Based on HR-Net to segment the damage image and quantify the damage volume, the irradiation doses 5.41, 9.55, 13.05, 20.85, 32.71, 52.92, 76.71, and 97.24 J/cm² corresponded to a damage volume of 4.58, 12.56, 16.74, 20.88, 24.52, 30.75, 34.13, and 37.32 mm³. The damage volume increased in a radiation dose-dependent manner.
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Affiliation(s)
- Changke Wang
- Beijing Institute of Radiation Medicine, 27 Taiping Road, 100850, Beijing, China
- College of Information Engineering, Henan University of Science and Technology, 263 Kaiyuan Avenue, 471023, Luoyang, China
| | - Qiong Ma
- Beijing Institute of Radiation Medicine, 27 Taiping Road, 100850, Beijing, China
| | - Yu Wei
- Beijing Institute of Radiation Medicine, 27 Taiping Road, 100850, Beijing, China
- College of Life Sciences, Hebei University, 180 East Wusi Road, 071000, Baoding, China
| | - Qi Liu
- Beijing Institute of Radiation Medicine, 27 Taiping Road, 100850, Beijing, China
| | - Yuqing Wang
- Beijing Institute of Radiation Medicine, 27 Taiping Road, 100850, Beijing, China
| | - Chenliang Xu
- Beijing Institute of Radiation Medicine, 27 Taiping Road, 100850, Beijing, China
- College of Information Engineering, Henan University of Science and Technology, 263 Kaiyuan Avenue, 471023, Luoyang, China
| | - Caihui Li
- Beijing Institute of Radiation Medicine, 27 Taiping Road, 100850, Beijing, China
| | - Qingyu Cai
- College of Information Engineering, Henan University of Science and Technology, 263 Kaiyuan Avenue, 471023, Luoyang, China
- Hunan SANY Industrial Vocational Technical College, Hanli Industrial Park, 410129, Changsha, China
| | - Haiyang Sun
- College of Information Engineering, Henan University of Science and Technology, 263 Kaiyuan Avenue, 471023, Luoyang, China
- Hunan SANY Industrial Vocational Technical College, Hanli Industrial Park, 410129, Changsha, China
| | - Xiaoan Tang
- College of Information Engineering, Henan University of Science and Technology, 263 Kaiyuan Avenue, 471023, Luoyang, China
| | - Hongxiang Kang
- Beijing Institute of Radiation Medicine, 27 Taiping Road, 100850, Beijing, China.
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Caruntu C, Ilie MA, Neagu M. Looking into the Skin in Health and Disease: From Microscopy Imaging Techniques to Molecular Analysis. Int J Mol Sci 2023; 24:13737. [PMID: 37762038 PMCID: PMC10531494 DOI: 10.3390/ijms241813737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 08/23/2023] [Indexed: 09/29/2023] Open
Abstract
The skin is a complex organ that includes a wide variety of tissue types with different embryological origins [...].
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Affiliation(s)
- Constantin Caruntu
- Department of Physiology, The “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania;
- Department of Dermatology, “Prof. N.C. Paulescu” National Institute of Diabetes, Nutrition and Metabolic Diseases, 011233 Bucharest, Romania
| | | | - Monica Neagu
- Faculty of Biology, University of Bucharest, Splaiul Independentei 91-95, 050095 Bucharest, Romania;
- Immunology Department, “Victor Babes” National Institute of Pathology, 050096 Bucharest, Romania
- Department of Pathology, Colentina University Hospital, 020125 Bucharest, Romania
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Kumar P, Dhara S, Gope A, Chatterjee J, Mandal S. Deep Learning based Skin-layer Segmentation for Characterizing Cutaneous Wounds from Optical Coherence Tomography Images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083666 DOI: 10.1109/embc40787.2023.10340321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Optical coherence tomography (OCT) is a medical imaging modality that allows us to probe deeper sub-structures of skin. The state-of-the-art wound care prediction and monitoring methods are based on visual evaluation and focus on surface information. However, research studies have shown that sub-surface information of the wound is critical for understanding the wound healing progression. This work demonstrated the use of OCT as an effective imaging tool for objective and non-invasive assessments of wound severity, the potential for healing, and healing progress by measuring the optical characteristics of skin components. We have demonstrated the efficacy of OCT in studying wound healing progress in vivo small animal models. Automated analysis of OCT datasets poses multiple challenges, such as limitations in the training dataset size, variation in data distribution induced by uncertainties in sample quality and experiment conditions. We have employed a U-Net-based model for segmentation of skin layers based on OCT images and to study epithelial and regenerated tissue thickness wound closure dynamics and thus quantify the progression of wound healing. In the experimental evaluation of the OCT skin image datasets, we achieved the objective of skin layer segmentation with an average intersection over union (IOU) of 0.9234. The results have been corroborated using gold-standard histology images and co-validated using inputs from pathologists.Clinical Relevance-To monitor wound healing progression without disrupting the healing procedure by superficial, non-invasive means via the identification of pixel characteristics of individual layers.
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