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Deep Learning for Analysis of Collagen Fiber Organization in Scar Tissue
- Title
- Deep Learning for Analysis of Collagen Fiber Organization in Scar Tissue
- Authors
- Pham, Thi Tram Anh; Kim, Hyeonsoo; Lee, Yeachan; Kang, Hyun Wook; Park, Suhyun
- Ewha Authors
- 박수현
- SCOPUS Author ID
- 박수현
- Issue Date
- 2021
- Journal Title
- IEEE ACCESS
- ISSN
- 2169-3536
- Citation
- IEEE ACCESS vol. 9, pp. 101755 - 101764
- Keywords
- Skin; Histopathology; Wounds; Optical fiber networks; Training; Laser modes; Deep learning; histology image; collagen fiber characterization; scar tissue classification
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
- Abstract
- The evaluation of the morphology and organization of collagen fibers is critical in understanding wound healing and tissue remodeling after a thermal injury of the skin. However, histological analysis conducted by pathologists is often labor-intensive and limited to qualitative evaluations and scoring within a narrow field of view. In this study, we propose a convolutional neural network (CNN) model to classify Masson's trichrome (MT)-stained histology images of burn-induced scar tissue and to characterize the microstructures of normal tissue and scar tissue in a quantitative manner. The scar tissue is created on in vivo rodent models and prepared for MT-stained histology slides after wound healing. A CNN model is developed, trained, and tested with various sizes of the histology images for classification and characterization. The proposed model classifies both normal tissue (i.e., without burn, as the control) and scar tissue at various scales with over 97% accuracy. The features acquired from the proposed CNN model visually characterizes the density and directional variance of the collagen fibers distributed in the dermal layers from whole histology images. The proposed deep learning technique can provide an objective and reliable method to rapidly assess and quantify wound repair and remodeling.
- DOI
- 10.1109/ACCESS.2021.3097370
- Appears in Collections:
- 공과대학 > 전자전기공학전공 > Journal papers
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