Deep-learning-based characterization of laser-induced scar tissue

Luluil Maknuna; Hyeonsoo Kim; Yeachan Lee; Yoonjin Choi; Hyunjung Kim; Myunggi Yi; Hyun Wook Kang

doi:10.1117/12.2626273

11 January 2022 Deep-learning-based characterization of laser-induced scar tissue

Luluil Maknuna, Hyeonsoo Kim, Yeachan Lee, Yoonjin Choi, Hyunjung Kim, Myunggi Yi, Hyun Wook Kang

Proceedings Volume 12159, SPIE Advanced Biophotonics Conference (SPIE ABC 2021); 121590G (2022) https://doi.org/10.1117/12.2626273
Event: SPIE Advanced Biophotonics Conference (SPIE ABC 2021), 2021, Busan, Republic of Korea

Abstract

Tissue analysis needs to determine the pathological properties that occur after wound healing process. Several staining techniques are widely used to understand the morphology of scar tissue, such as staining with hematoxylin and eosin (HE), picrosirius red, and Masson’s Trichome. In spite of the common staining technique, the tissue staining using HE has several limitations, such as labor-intensive, time-consuming, high memory and cost. Due to the limited of view, using the whole slide image is quite challenging to analyze the scar lesion. Hence, we developed a deep learning technique to simultaneously classify and characterize a scar lesion in the whole slide image, based on object instance segmentation. The deep learning trained the patterns from the data representation through neural network and convolution equations. The proposed technique recognized 384 images in less than a minute with 99.9% accuracy. Based on classification, quantitative analysis was performed to confirm the recognition of the scar based on the important features, such as collagen density and directional variance of collagen in scar area. After created the density map and directional variance map of collagen, the differences were almost 50% in normal and scar tissue. Therefore, the proposed deep learning method can be time- and cost-effective to characterize the pathological feature of scar tissue for the objective histological analysis. The analyses are expected to optimize various therapeutic methods to reduce or even eliminate scars on the skin.

Citation Download Citation

Luluil Maknuna, Hyeonsoo Kim, Yeachan Lee, Yoonjin Choi, Hyunjung Kim, Myunggi Yi, and Hyun Wook Kang "Deep-learning-based characterization of laser-induced scar tissue", Proc. SPIE 12159, SPIE Advanced Biophotonics Conference (SPIE ABC 2021), 121590G (11 January 2022); https://doi.org/10.1117/12.2626273

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KEYWORDS

Collagen

Tissues

Image segmentation

Laser tissue interaction

RGB color model

Convolution

Wound healing

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