Tissue classification and segmentation of pressure injuries using convolutional neural networks. (June 2018)

• Record Type: Journal Article
• Authors: Zahia, Sofia; Sierra-Sosa, Daniel; Garcia-Zapirain, Begonya; Elmaghraby, Adel
• Is Part Of: Computer methods and programs in biomedicine. Volume 159(2018)
• Page Start: 51
• Page End: 58
• Publication Date: 2018-06
• Subjects: Deep learning -- Pressure injuries -- Tissue type classification -- Image segmentation -- Convolutional neural networks; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.02.018
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: In this paper we presented an approach for automatic tissue segmentation using a Convolutional Neural Network. The methodology is based on the classification of different tissue types: Necrotic, granulation and slough. We present different metrics to evaluate our approach, obtaining an overall average classification accuracy of 92.01%, an average total weighted Dice Similarity Coefficient of 91.38%. An average precision per class of 97.31% for granulation tissue, 96.59% for necrotic tissue, and 77.90% for slough tissue were obtained. By using this methodology, we were able segment complicated structures within the image to be recognized, providing a robust method for Pressure Injuries assess. Abstract: Background and Objectives: This paper presents a new approach for automatic tissue classification in pressure injuries. These wounds are localized skin damages which need frequent diagnosis and treatment. Therefore, a reliable and accurate systems for segmentation and tissue type identification are needed in order to achieve better treatment results. Methods: Our proposed system is based on a Convolutional Neural Network (CNN) devoted to performing optimized segmentation of the different tissue types present in pressure injuries (granulation, slough, and necrotic tissues). A preprocessing step removes the flash light and creates a set of 5x5 sub-images which are used as input for the CNN network. The network output will classify every sub-image of the validation set into one of the three classes studied. Results: The metrics used to evaluate our approach show an overall average classification accuracy of 92.01%, an average total weighted Dice Similarity Coefficient of 91.38%, and an average precision per class of 97.31% for granulation tissue, 96.59% for necrotic tissue, and 77.90% for slough tissue. Conclusions: Our system has been proven to make recognition of complicated structures in biomedical images feasible.