Abstract
In dermatology, the histological examination of skin cross-sections is essential for skin cancer diagnosis and treatment planning. However, the complete coverage of tissue abnormalities is not possible due to time constraints as well as the sheer number of cell groups. We present an automatic segmentation approach of seven tissue classes: vessels, perspiration glands, hair follicles, sebaceous glands, tumor tissue, epidermis and fatty tissue, for a fast processing of the large datasets. Hence, the initial size of the data lends itself to the use of patch-based deep learning models, resulting in good IoU score of 94.2 percent for the cancerous tissue and overall IoU score of 83.6 percent.
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References
WCRF International: skin cancer statistics. 2022.
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM et al. Dermatologist-level classification of skin cancer with deep neural networks. Nat. 2017;542(7639):115–8.
Saginala K, Barsouk A, Aluru JS, Rawla P, Barsouk A. Epidemiology of melanoma. Med Sci. 2021;9(4):63.
Tronnier M, Smolle J,Wolff HH. Ultraviolet irradiation induces acute changes in melanocytic nevi. J Inv Dermatol. 1995;104(4):475–8.
Serag A, Ion-Margineanu A, Qureshi H, McMillan R, Saint Martin MJ, Diamond J et al. Translational AI and deep learning in diagnostic pathology. Front Med. 2019;6:185.
Kather JN,Weis CA, Bianconi F, Melchers SM, Schad LR, Gaiser T et al. Multi-class texture analysis in colorectal cancer histology. Sci Rep. 2016;6(1):27988.
Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. Proc MICCAI. Springer. 2015:234–41.
Lin TY, Goyal P, Girshick R, He K, Dollár P. Focal loss for dense object detection. Proc IEEE. 2017:2980–8.
Zhao H, Shi J, Qi X,Wang X, Jia J. Pyramid scene parsing network. Proc IEEE. 2017:2881– 90.
Wang L, Shao A, Huang F, Liu Z, Wang Y, Huang X et al. Deep learning-based semantic segmentation of non-melanocytic skin tumors in whole-slide histopathological images. Exp Dermatol. 2023.
Liu Y, He Q, Duan H, Shi H, Han A, He Y. Using sparse patch annotation for tumor segmentation in histopathological images. Sensors. 2022;22(16):6053.
Li YJ, Chou HH, Lin PC, Shen MR, Hsieh SY. A novel deep learning-based algorithm combining histopathological features with tissue areas to predict colorectal cancer survival from whole-slide images. J Transl Med. 2023;21(1):731.
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© 2024 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Kreher, R. et al. (2024). Advanced Deep Learning for Skin Histoglyphics at Cellular Level. In: Maier, A., Deserno, T.M., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2024. BVM 2024. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-44037-4_20
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DOI: https://doi.org/10.1007/978-3-658-44037-4_20
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