Automated recognition and quantitative analysis of scar tissue using deep learning

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

doi:10.1117/12.2610591

7 March 2022 Automated recognition and quantitative analysis of scar tissue using deep learning

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

Author Affiliations +

Proceedings Volume PC11958, Optical Interactions with Tissue and Cells XXXIII; and Advanced Photonics in Urology; PC119580Q (2022) https://doi.org/10.1117/12.2610591
Event: SPIE BiOS, 2022, San Francisco, California, United States

Conference Poster

Abstract

Tissue analysis is pivotal research to determine the pathological properties that occur after the wound healing process. Several staining techniques to understand the morphology of scar tissue are widely used, such as staining with HE (Hematoxylin and Eosin), picrosirius red, and Masson's Trichome. Tissue staining using hematoxylin and eosin has several limitations: labor-intensive, time-consuming, high memory, and cost. Besides that, used a whole slide image to analyze the scar lesion can be more challenging. Hence, we used deep learning to automatically classify and localize scar lesions in the whole slide image based on object instance segmentation. Deep learning trained the patterns from the data representation through a neural network and convolution equations. Deep learning recognized 384 images in less than a minute with 99.89% accuracy. 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. In addition to confirming the scar's recognition in the qualitative analysis, the authors also performed a quantitative analysis to obtain information from the scar tissue, such as collagen density from color extraction and collagen directional variance. Segmentation analysis is also used to determine the morphological structure in scar tissue compared to normal tissue. The analysis results can determine various further therapeutic methods to reduce or even eliminate scars on urological tissues in future works.

Conference Presentation

Citation Download Citation

Luluil Maknuna, Hyeonsoo Kim, Yeachan Lee, Yoon Jin Choi, Hyunjung Kim, Myunggi Yi, and Hyun Wook Kang "Automated recognition and quantitative analysis of scar tissue using deep learning", Proc. SPIE PC11958, Optical Interactions with Tissue and Cells XXXIII; and Advanced Photonics in Urology, PC119580Q (7 March 2022); https://doi.org/10.1117/12.2610591

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