Internet service for wound area measurement using digital planimetry with adaptive calibration and image segmentation with deep convolutional neural networks

• Autorzy: Foltynski Piotr , Ladyzynski Piotr

Identyfikatory

DOI

10.1016/j.bbe.2022.11.004

• Języki publikacji: EN

Abstrakty

EN

Uncontrolled diabetes leads to serious complications comparable to cancer. Infected foot ulcer causes a 5-year mortality of 50%. Proper treatment of foot wounds is essential, and wound area monitoring plays an important role in this area. In this article, we describe an automatic wound area measurement service that facilitates area measurement and the measurement result is based on adaptive calibration for larger accuracy at curved surfaces. Users need to take a digital picture of a wound and calibration markers and send them for analysis using an Internet page. The deep learning model based on convolutional neural networks (CNNs) was trained using 565 wound images and was used for image segmentation to identify the wound and calibration markers. The developed software calculates the wound area based on the number of pixels in the wound region and the calibration coefficient determined from distances between ticks at calibration markers. The result of the measurement is sent back to the user at the provided e-mail address. The median relative error of wound area measurement in the wound models was 1.21%. The efficacy of the CNN model was tested on 41 wounds and 73 wound models. The averaged values for the dice similarity coefficient, intersection over union, accuracy and specificity for wound identification were 90.9%, 83.9%, 99.3% and 99.6%, respectively. The service proved its high efficacy and can be used in wound area monitoring. The service may be used not only by health care specialists but also by patients. Thus, it is important tool for wound healing monitoring.

Słowa kluczowe

EN

diabetic foot ulcer; wound area measurement; digital planimetry; deep learning; convolutional neural network

PL

zespół stopy cukrzycowej; pomiar obszaru rany; planimetria cyfrowa; uczenie głębokie; sieć neuronowa konwolucyjna

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2023
• Tom: Vol. 43, no. 1
• Strony: 17--29
• Opis fizyczny: Bibliogr. 47 poz., rys., tab., wykr.

Twórcy

autor

Foltynski Piotr

• Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, 02-109 Warsaw, Poland

autor

Ladyzynski Piotr

• Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland

Bibliografia

• [1] International Diabetes Federation. IDF Diabetes Atlas. 10th edn. Brussels, Belgium: International Diabetes Federation; 2021.
• [2] Nathan DM; DCCT/EDIC Research Group. The diabetes control and complications trial/epidemiology of diabetes interventions and complications study at 30 years: overview. Diabetes Care. 2014;37(1):9-16. doi:10.2337/dc13-2112.
• [3] Abbott CA, Carrington AL, Ashe H, Bath S, Every LC, Griffiths J, et al. The North-West Diabetes Foot Care Study: incidence of, and risk factors for, new diabetic foot ulceration in a community-based patient cohort. Diabet Med 2002;19 (5):377-84.
• [4] Lauterbach S, Kostev K, Kohlmann T. Prevalence of diabetic foot syndrome and its risk factors in the UK. J Wound Care 2010;19(8):333-7.
• [5] Armstrong DG, Swerdlow MA, Armstrong AA, Conte MS, Padula WV, Bus SA. Five year mortality and direct costs of care for people with diabetic foot complications are comparable to cancer. J Foot Ankle Res 2020;13:16. https://doi. org/10.1186/s13047-020-00383-2.
• [6] Vuorlaakso M, Kiiski J, Salonen T, Karppelin M, Helminen M, Kaartinen I. Major Amputation Profoundly Increases Mortality in Patients With Diabetic Foot Infection. Front Surg 2021 Apr;30(8) 655902.
• [7] Flores AM, Mell MW, Dalman RL, Chandra V. Benefit of multidisciplinary wound care center on the volume and outcomes of a vascular surgery practice. J Vasc Surg 2019;70 (5):1612-9.
• [8] Steed DL, Attinger C, Colaizzi T, Crossland M, Franz M, Harkless L, et al. Guidelines for the treatment of diabetic ulcers. Wound Repair Regen 2006;14(6):680-92.
• [9] Hingorani A, LaMuraglia GM, Henke P, Meissner MH, Loretz L, Zinszer KM, et al. The management of diabetic foot: A clinical practice guideline by the Society for Vascular Surgery in collaboration with the American Podiatric Medical Association and the Society for Vascular Medicine. J Vasc Surg 2016;63(2):3S-21S.
• [10] Jørgensen LB, Sørensen JA, Jemec GB, Yderstraede KB. Methods to assess area and volume of wounds - a systematic review. Int Wound J 2016;13:540-53. https://doi.org/10.1111/ iwj.12472.
• [11] Wendland DM, Taylor DW. Wound measurement tools and techniques: A review. Journal of Acute Care Physical Therapy 2017;8:42-57.
• [12] Foltynski P, Ciechanowska A, Ladyzynski P. Wound surface area measurement methods. Biocyber Biomed Eng 2021;41:1454-65. https://doi.org/10.1016/j.bbe.2021.04.011.
• [13] Foltynski P. Ways to increase precision and accuracy of wound area measurement using smart devices: Advanced app Planimator. PLOS ONE 2018;13:e0192485. https://doi.org/ 10.1371/journal.pone.0192485.
• [14] Foltynski P, Ladyzynski P. Digital Planimetry With a New Adaptive Calibration Procedure Results in Accurate and Precise Wound Area Measurement at Curved Surfaces. J Diabetes Sci Technol 2022;16:128-36. https://doi.org/10.1177/ 1932296820959346.
• [15] Noh H, Hong S, Han B. Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision. 2015; pp:1520-1528.
• [16] Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM 2017;60:84-90. https://doi.org/10.1145/3065386.
• [17] Kaur A, Singh Y, Neeru N, Kaur L, Singh A. A Survey on Deep Learning Approaches to Medical Images and a Systematic Look up into Real-Time Object Detection. Arch Computat Methods Eng 2022;29(4):2071-111.
• [18] Rizwan I Haque I, Neubert J. Deep learning approaches to biomedical image segmentation. Inf Med Unlocked 2020;18:100297.
• [19] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (Eds.), MICCAI 2015. LNCS, vol. 9351, pp. 234-241. Springer, Cham (2015). Doi:10.1007/978-3-319-24574-4_28.
• [20] Wang C, Anisuzzaman DM, Williamson V, Dhar MK, Rostami B, Niezgoda J, et al. Fully automatic wound segmentation with deep convolutional neural networks. Sci Rep 2020;10:21897.
• [21] Niri R, Hassan D, Lucas Y, Treuillet S. Semantic Segmentation of Diabetic Foot Ulcer Images: Dealing with Small Dataset in DL Approaches. In: El Moataz A., Mammass D., Mansouri A., Nouboud F. (Eds.), Image and Signal Processing. ICISP 2020. Lecture Notes in Computer Science, vol 12119. Springer, Cham.
• [22] Foltynski P, Ladyzynski P, Migalska-Musial K, Sabalinska S, Ciechanowska A, Wojcicki J. A New Imaging and Data Transmitting Device for Telemonitoring of Diabetic Foot Syndrome Patients. Diab Tech & Ther 2011;13(8):861-7.
• [23] Foltynski P, Wojcicki JM, Ladyzynski P, Migalska-Musial K, et al. Monitoring of diabetic foot syndrome treatment: some new perspectives. Artif Organs 2011;35(8):176-82.
• [24] Ladyzynski P, Foltynski P, Molik M, Tarwacka J, Migalska-Musial K, Mlynarczuk M, et al. Area of the diabetic ulcers estimated applying a foot scanner-based home telecare system and three reference methods. DiabetesTechnol Ther 2011;13(11):1101-7.
• [25] Molik M, Foltynski P, Ladyzynski P, Tarwacka J, Migalska-Musial K, Ciechanowska A, et al. Comparison of the wound area assessment methods in the diabetic foot syndrome. Biocybernet Biomed Eng 2010;30:3-15.
• [26] VGG Image Annotator, https://www.robots.ox.ac.uk/ ~vgg/software/via/via-1.0.6.html, Last accessed 2022-06-01.
• [27] Keras API, https://keras.io/, Last accessed 2022-06-01.
• [28] Image segmentation with a U-Net-like architecture, https:// keras.io/examples/vision/oxford_pets_image_segmentation/, Last accessed 2022-06-01.
• [29] AutoPlanimator service, https://autoplanimator.ibib.waw.pl, Last accessed 2022-06-01.
• [30] Privalov M, Beisemann N, Barbari JE, Mandelka E, Müller M, Syrek H, et al. Software-Based Method for Automated Segmentation and Measurement of Wounds on Photographs Using Mask R-CNN: a Validation Study. J Digit Imaging 2021;34(4):788-97.
• [31] Tharwat A. Classification assessment methods. Applied Computing and Informatics 2021;17(1):168-92.
• [32] Chino DY, Scabora LC, Cazzolato MT, Jorge AE, Traina-Jr C, Traina AJ. Segmenting skin ulcers and measuring the wound area using deep convolutional networks. Comput Methods Programs Biomed 2020 Jul;1(191):105376.
• [33] Cazzolato MT, Ramos JS, Rodrigues LS, Scabora LC, Chino DYT, Jorge AES, et al. The UTrack framework for segmenting and measuring dermatological ulcers through telemedicine. Comput Biol Med 2021;134:104489.
• [34] Carrión H, Jafari M, Bagood MD, Yang H-y, Isseroff RR, Gomez M, et al. Automatic wound detection and size estimation using deep learning algorithms. PLoS Comput Biol 2022;18(3): e1009852.
• [35] Goyal M, Yap MH, Reeves ND, Rajbhandari S and Spragg J. Fully convolutional networks for diabetic foot ulcer segmentation, In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, AB, 2017, pp. 618-623.
• [36] Jiao C, Su K, Xie W, Ye Z. Burn image segmentation based on mask regions with convolutional neural network deep learning framework: more accurate and more convenient. Burn Trauma 2019;7:1-14. https://doi.org/10.1186/s41038-018-0137-9.
• [37] Tomas S. Stock pictures of wounds. Medetec Wound Last accessed 2022-01-12. Database 2020. Available from: http:// medetec.co.uk/files/medetec-image-databases.html,.
• [38] Marijanović D, Nyarko EK, Filko D. Wound Detection by Simple Feedforward Neural Network. Electronics 2022;11 (3):329. https://doi.org/10.3390/electronics11030329.
• [39] Scebba G, Zhang J, Catanzaro S, Mihai C, Distler O, Berli M, et al. Detect-and-segment: A deep learning approach to automate wound image segmentation. Inf Med Unlocked 2022;29:100884. https://doi.org/10.1016/j.imu.2022.100884.
• [40] Ramachandram D, Ramirez-GarciaLuna JL, Fraser RDJ, Martínez-Jiménez MA, Arriaga-Caballero JE, Allport J. Fully Automated Wound Tissue Segmentation Using Deep Learning on Mobile Devices: Cohort Study. JMIR Mhealth Uhealth 2022;10(4):e36977.
• [41] Ohura N, Mitsuno R, Sakisaka M, Terabe Y, Morishige Y, Uchiyama A, et al. Convolutional neural networks for wound detection: the role of artificial intelligence in wound care. J Wound Care 2019;28(Sup10):S13-24.
• [42] Sarp S, Kuzlu M, Pipattanasomporn M, Guler O. Simultaneous wound border segmentation and tissue classification using a conditional generative adversarial network. The Journal of Engineering 2021;2021(3):125-34.
• [43] Niri R, Gutierrez E, Douzi H, Lucas Y, Treuillet S, Castaneda B, et al. Multi-View Data Augmentation to Improve Wound Segmentation on 3D Surface Model by Deep Learning. IEEE Access 2021;9:157628-38.
• [44] Hüsers J, Hafer G, Heggemann J, Wiemeyer S, Przysucha M, Dissemond J, et al. Automatic Classification of Diabetic Foot Ulcer Images - A Transfer-Learning Approach to Detect Wound Maceration. Stud Health Technol Inform 2022 Jan;14 (289):301-4.
• [45] Schumaker G, Becker A, An G, Badylak S, Johnson S, Jiang P, et al. Optical Biopsy Using a Neural Network to Predict Gene Expression From Photos of Wounds. J Surg Res 2022;270:547-54.
• [46] Liu Z, Agu EO, Pedersen PC, Lindsay C, Tulu B, Strong D. Comprehensive Assessment of Fine-Grained Wound Images Using a Patch-Based CNN With Context-Preserving Attention. IEEE Open Journal of Engineering in Medicine and Biology 2021;2:224-34.
• [47] Foltynski P, Ladyzynski P, Ciechanowska A, Migalska-Musial K, Judzewicz G, Sabalinska S, et al. Wound Area Measurement with Digital Planimetry: Improved Accuracy and Precision with Calibration Based on 2 Rulers. PLoS One 2015;10(8):e0134622. https://doi.org/10.1371/journal. Pone.013462.

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)