Encoder–decoder semantic segmentation models for pressure wound images

References

• Sen CK, Gordillo GM, Roy S, et al. Human skin wounds: a major and snow-balling threat to public health and the economy. Wound Repair Regen. 2009;17(6):763–771.
   PubMed Web of Science ®Google Scholar
• Fauzi MFA, Khansa I, Catignani K, et al. Computerized segmentation and measurement of chronic wound images. Comput Biol Med. 2015;60:74–85.
   PubMed Web of Science ®Google Scholar
• Mervis JS, Phillips TJ. Pressure ulcers: pathophysiology: epidemiology, risk factors, and presentation. J Am Acad Dermatol. 2019;81(4):881–890.
   PubMed Web of Science ®Google Scholar
• Alipoor E, Mehrdadi P, Yaseri M, et al. Association of overweight and obesity with the prevalence and incidence of pressure ulcers: a systematic review and meta-analysis. Clin Nutr. 2021;40(9):5089–5098.
   PubMed Web of Science ®Google Scholar
• Yilmazer T, Tuzer H. The effect of a pressure ulcer prevention care bundle on nursing workload costs. J Tissue Viability. 2022;31(3):459–464.
   PubMed Web of Science ®Google Scholar
• Kim J, Lee JY, Lee E. Risk factors for newly acquired pressure ulcer and the impact of nurse staffing on pressure ulcer incidence. J Nurs Manag. 2022;30(5):O1–O9.
   PubMed Web of Science ®Google Scholar
• Morrison A, Madden C, Messmer J, Management of chronic wounds. Primary Care: Clinics in Office Practice. 2022;49(1):85–98.
   PubMed Web of Science ®Google Scholar
• Seo H, Badiei Khuzani M, Vasudevan V, et al. Machine learning techniques for biomedical image segmentation: an overview of technical aspects and introduction to state-of-art applications. Med Phys. 2020;47(5):e148–e167.
   PubMed Web of Science ®Google Scholar
• Singh S, Mittal N, Thakur D, et al. Nature and biologically inspired image segmentation techniques. Arch Comput Methods Eng. 2022;29:1415–1442.
   Web of Science ®Google Scholar
• Voulodimos A, Doulamis N, Doulamis A, et al. Deep learning for computer vision: a brief review. Comput Intell Neurosci. 2018;2018: article id 7068349. DOI:10.1155/2018/7068349
   Web of Science ®Google Scholar
• O’Mahony N, Campbell S, Carvalho A, et al. Deep learning vs. traditional computer vision. Science and Information Conference; Springer; 2019.
   Google Scholar
• Bauer K, Rock K, Nazzal M, et al. Pressure ulcers in the United States’ inpatient population from 2008 to 2012: results of a retrospective nationwide study. Ostomy Wound Manage. 2016;62(11):30–38.
   PubMed Web of Science ®Google Scholar
• Frykberg RG, Banks J. Challenges in the treatment of chronic wounds. Adv Wound Care. 2015;4(9):560–582.
   PubMed Web of Science ®Google Scholar
• Tubaishat A, Papanikolaou P, Anthony D, et al. Pressure ulcers prevalence in the acute care setting: a systematic review, 2000–2015. Clin Nurs Res. 2018;27(6):643–659.
   PubMed Web of Science ®Google Scholar
• Sen CK. (2019). Human wounds and its burden: an updated compendium of estimates, Mary Ann Liebert, Inc., publishers 140 Huguenot Street, 3rd Floor New … .
   Google Scholar
• Alderden J, Rondinelli J, Pepper G, et al. Risk factors for pressure injuries among critical care patients: a systematic review. Int J Nurs Stud. 2017;71:97–114.
   PubMed Web of Science ®Google Scholar
• Sundelius C. (2018). Deep fusion of imaging modalities for semantic segmentation of satellite imagery.
   Google Scholar
• Cheng D, Liao R, Fidler S, et al. Darnet: deep active ray network for building segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2019.
   Google Scholar
• Dong X, Niu J, Cui J, et al. Fast segmentation-based object tracking model for autonomous vehicles. International Conference on Algorithms and Architectures for Parallel Processing; Springer; 2020.
   Google Scholar
• Shen D, Wu G, Suk H-I, et al. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19:221–248.
   PubMed Web of Science ®Google Scholar
• Hesamian MH, Jia W, He X, et al. Deep learning techniques for medical image segmentation: achievements and challenges. J Digit Imaging. 2019;32(4):582–596.
   PubMed Web of Science ®Google Scholar
• Tajbakhsh N, Jeyaseelan L, Li Q, et al. Embracing imperfect datasets: a review of deep learning solutions for medical image segmentation. Med Image Anal. 2020;63:101693.
   PubMed Web of Science ®Google Scholar
• Qureshi I, Yan J, Abbas Q, et al. Medical image segmentation using deep semantic-based methods: a review of techniques, applications and emerging trends. Inf Fusion. 2023;90:316–352.
   Web of Science ®Google Scholar
• Anisuzzaman D, Wang C, Rostami B, et al. Image based artificial intelligence in wound assessment: a systematic review. arXiv preprint arXiv:2009.07141. 2020.
   Google Scholar
• Zahia S, Zapirain MBG, Sevillano X, et al. Pressure injury image analysis with machine learning techniques: a systematic review on previous and possible future methods. Artif Intell Med. 2020;102:101742.
   PubMed Web of Science ®Google Scholar
• Kolesnik M, Fexa A. (2004). Segmentation of wounds in the combined color-texture feature space. Medical imaging 2004: image processing: International Society for Optics and Photonics.
   Google Scholar
• Kolesnik M, Fexa A. (2005). Multi-dimensional color histograms for segmentation of wounds in images. International Conference Image Analysis and Recognition, Springer.
   Google Scholar
• Wang L, Pedersen PC, Agu E, et al. Area determination of diabetic foot ulcer images using a cascaded two-stage SVM-based classification. IEEE Trans Biomed Eng. 2016;64(9):2098–2109.
   PubMedGoogle Scholar
• Kolesnik M, Fexa A. (2006). How robust is the SVM wound segmentation? Proceedings of the 7th Nordic Signal Processing Symposium-NORSIG 2006, IEEE.
   Google Scholar
• Wantanajittikul K, Auephanwiriyakul S, Theera-Umpon N, et al. Automatic segmentation and degree identification in burn color images. The 4th 2011 Biomedical Engineering International Conference; IEEE; 2012.
   Google Scholar
• Song B, Sacan A. (2012). Automated wound identification system based on image segmentation and artificial neural networks. 2012 IEEE International Conference on bioinformatics and biomedicine, IEEE.
   Google Scholar
• Hani AFM, Arshad L, Malik AS, et al. Haemoglobin distribution in ulcers for healing assessment. 2012 4th International Conference on Intelligent and Advanced Systems (ICIAS2012); IEEE; 2012.
   Google Scholar
• Chakraborty C. Computational approach for chronic wound tissue characterization. Inform Med Unlocked. 2019;17:100162.
   Google Scholar
• Yadav MK, Manohar DD, Mukherjee G, et al. Segmentation of chronic wound areas by clustering techniques using selected color space. J Med Imaging Health Inform. 2013;3(1):22–29.
   Web of Science ®Google Scholar
• Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88.
   PubMed Web of Science ®Google Scholar
• Gul S, Khan MS, Bibi A, et al. Deep learning techniques for liver and liver tumor segmentation: a review. Comput Biol Med. 2022;105620.
   PubMedGoogle Scholar
• Maiti S, Maji D, Dhara AK, et al. Automatic detection and segmentation of optic disc using a modified convolution network. Biomed Signal Process Control. 2022;76:103633.
   Web of Science ®Google Scholar
• Prangemeier T, Wildner C, Françani AO, et al. Yeast cell segmentation in microstructured environments with deep learning. Biosystems. 2022;211:104557.
   PubMed Web of Science ®Google Scholar
• Lei T, Wang R, Wan Y, et al. Medical image segmentation using deep learning: a survey. arXiv preprint arXiv:2009.13120. 2020.
   Google Scholar
• Peng T, Gu Y, Ye Z, et al. A-LugSeg: automatic and explainability-guided multi-site lung detection in chest X-ray images. Expert Syst Appl. 2022;198:116873.
   Web of Science ®Google Scholar
• Garcia-Garcia A, Orts-Escolano S, Oprea S, et al. A review on deep learning techniques applied to semantic segmentation. arXiv preprint arXiv:1704.06857. 2017.
   Google Scholar
• Ghosh S, Das N, Das I, et al. Understanding deep learning techniques for image segmentation. ACM Computing Surveys (CSUR). 2019;52(4):1–35.
   Web of Science ®Google Scholar
• Hao S, Zhou Y, Guo Y. A brief survey on semantic segmentation with deep learning. Neurocomputing. 2020;406:302–321.
   Web of Science ®Google Scholar
• Minaee S, Boykov Y, Porikli F, et al. Image segmentation using deep learning: a survey. arXiv preprint arXiv:2001.05566. 2020.
   Google Scholar
• Alzubaidi L, Zhang J, Humaidi AJ, et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data. 2021;8(1):1–74.
   PubMedGoogle Scholar
• Suganyadevi S, Seethalakshmi V, Balasamy K, et al. A review on deep learning in medical image analysis. Int J Multimed Inf Retr. 2022;11(1):19–38.
   PubMed Web of Science ®Google Scholar
• Wang R, Lei T, Cui R, et al. Medical image segmentation using deep learning: a survey. IET Image Proc. 2022;16(5):1243–1267.
   Web of Science ®Google Scholar
• Zhang R, Tian D, Xu D, et al. A survey of wound image analysis using deep learning: classification, detection, and segmentation. IEEE Access. 2022;10:79502–79515.
   Google Scholar
• Bose S, Chowdhury RS, Das R, et al. Dense dilated deep multiscale supervised U-network for biomedical image segmentation. Comput Biol Med. 2022;143:105274.
   PubMed Web of Science ®Google Scholar
• Havaei M, Davy A, Warde-Farley D, et al. Brain tumor segmentation with deep neural networks. Med Image Anal. 2017;35:18–31.
   PubMed Web of Science ®Google Scholar
• Wang G, Li W, Ourselin S, et al. Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. International MICCAI Brainlesion Workshop; Springer; 2017.
   Google Scholar
• Kaur R, Doegar A. Brain tumor segmentation using deep learning: taxonomy: survey and challenges. Brain Tumor MRI Image Segmentation Using Deep Learning Techniques, Elsevier; 2022. p. 225–238.
   Google Scholar
• Wang C, Yan X, Smith M, et al. A unified framework for automatic wound segmentation and analysis with deep convolutional neural networks. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); IEEE; 2015.
   Google Scholar
• Goyal M, Yap MH, Reeves ND, et al. Fully convolutional networks for diabetic foot ulcer segmentation. 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC); IEEE; 2017.
   Google Scholar
• Liu X, Wang C, Li F, et al. A framework of wound segmentation based on deep convolutional networks. 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI); IEEE; 2017.
   Google Scholar
• Li F, Wang C, Liu X, et al. A composite model of wound segmentation based on traditional methods and deep neural networks. Comput Intell Neurosci. 2018;2018. DOI:10.1155/2018/4149103
   Web of Science ®Google Scholar
• Zahia S, Sierra-Sosa D, Garcia-Zapirain B, et al. Tissue classification and segmentation of pressure injuries using convolutional neural networks. Comput Methods Programs Biomed. 2018;159:51–58.
   PubMed Web of Science ®Google Scholar
• Ramachandram D, Ramirez-GarciaLuna JL, Fraser RD, et al. Fully automated wound tissue segmentation using deep learning on mobile devices: cohort study. JMIR Mhealth Uhealth. 2022;10(4):e36977.
   PubMed Web of Science ®Google Scholar
• Zahia S, Garcia-Zapirain B, Elmaghraby A, Integrating 3D model representation for an accurate non-invasive assessment of pressure injuries with deep learning. Sensors. 2020;20(10):2933.
   PubMed Web of Science ®Google Scholar
• Ohura N, Mitsuno R, Sakisaka M, et al. Convolutional neural networks for wound detection: the role of artificial intelligence in wound care. J Wound Care. 2019;28(Sup10):S13–S24.
   PubMedGoogle Scholar
• Chang CW, Christian M, Chang DH, et al. Deep learning approach based on superpixel segmentation assisted labeling for automatic pressure ulcer diagnosis. PLoS One. 2022;17(2):e0264139.
   PubMed Web of Science ®Google Scholar
• Niri R, Gutierrez E, Douzi H, et al. Multi-view data augmentation to improve wound segmentation on 3D surface model by deep learning. IEEE Access. 2021;9:157628–157638.
   Google Scholar
• Scebba G, Zhang J, Catanzaro S, et al. Detect-and-segment: a deep learning approach to automate wound image segmentation. Inform Med Unlocked. 2022;29:100884.
   Google Scholar
• Ong EP, Yin CTK, Lee B-H, Efficient deep learning-based wound-bed segmentation for mobile applications. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC); IEEE; 2020.
   Google Scholar
• Long J, Shelhamer E, Darrell T, Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2015.
   Google Scholar
• Zhao H, Shi J, Qi X, et al. Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2017.
   Google Scholar
• Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-assisted Intervention; Springer; 2015.
   Google Scholar
• Badrinarayanan V, Kendall A, Cipolla R. Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell. 2017;39(12):2481–2495.
   PubMed Web of Science ®Google Scholar
• Chen L-C, Papandreou G, Kokkinos I, et al. Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell. 2017;40(4):834–848.
   PubMed Web of Science ®Google Scholar
• He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2016.
   Google Scholar
• Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. 2014.
   Google Scholar
• Howard AG, Zhu M, Chen B, et al. Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861. 2017.
   Google Scholar
• Tan M, Le Q. (2019). Efficientnet: rethinking model scaling for convolutional neural networks. International Conference on Machine Learning, PMLR.
   Google Scholar
• Alzubaidi L, Fadhel MA, Al-Shamma O, et al. Towards a better understanding of transfer learning for medical imaging: a case study. Appl Sci. 2020;10(13):4523.
   Google Scholar
• Alzubaidi L, Al-Amidie M, Al-Asadi A, et al. Novel transfer learning approach for medical imaging with limited labeled data. Cancers (Basel). 2021;13(7):1590.
   PubMed Web of Science ®Google Scholar
• Azizi S, Mustafa B, Ryan F, et al. Big self-supervised models advance medical image classification. Proceedings of the IEEE/CVF International Conference on Computer Vision; 2021.
   Google Scholar
• Alzubaidi L, Fadhel MA, Al-Shamma Q, et al. Robust application of new deep learning tools: an experimental study in medical imaging. Multimed Tools Appl. 2022;81(10):13289–13317.
   Web of Science ®Google Scholar
• Steve T. (2021). Medetec pressure wound dataset. http://www.medetec.co.uk/slide scans/pressure-ulcer-images-a/index.html, http://www.medetec.co.uk/slide scans/pressure-ulcer-images-b/index.html
   Google Scholar
• Steve T. (2021). Medetec wound database. http://www.medetec.co.uk/files/medetec-image-databases.html
   Google Scholar
• Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning. J Big Data. 2019;6(1):1–48.
   Google Scholar
• Kingma DP, Ba J. Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980. 2014.
   Google Scholar
• Zhang X, Liu C-A. Model averaging prediction by K-fold cross-validation. J Econom. 2022. DOI:10.1016/j.jeconom.2022.04.007
   Google Scholar
• Fawcett T. An introduction to ROC analysis. Pattern Recognit Lett. 2006;27(8):861–874.
   Web of Science ®Google Scholar