{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T07:14:15Z","timestamp":1774682055231,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,20]],"date-time":"2021-11-20T00:00:00Z","timestamp":1637366400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007637","name":"Colciencias","doi-asserted-by":"publisher","award":["111084467950"],"award-info":[{"award-number":["111084467950"]}],"id":[{"id":"10.13039\/100007637","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002753","name":"National University of Colombia","doi-asserted-by":"publisher","award":["51175"],"award-info":[{"award-number":["51175"]}],"id":[{"id":"10.13039\/501100002753","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Peripheral nerve blocking (PNB) is a standard procedure to support regional anesthesia. Still, correct localization of the nerve\u2019s structure is needed to avoid adverse effects; thereby, ultrasound images are used as an aid approach. In addition, image-based automatic nerve segmentation from deep learning methods has been proposed to mitigate attenuation and speckle noise ultrasonography issues. Notwithstanding, complex architectures highlight the region of interest lacking suitable data interpretability concerning the learned features from raw instances. Here, a kernel-based deep learning enhancement is introduced for nerve structure segmentation. In a nutshell, a random Fourier features-based approach was utilized to complement three well-known semantic segmentation architectures, e.g., fully convolutional network, U-net, and ResUnet. Moreover, two ultrasound image datasets for PNB were tested. Obtained results show that our kernel-based approach provides a better generalization capability from image segmentation-based assessments on different nerve structures. Further, for data interpretability, a semantic segmentation extension of the GradCam++ for class-activation mapping was used to reveal relevant learned features separating between nerve and background. Thus, our proposal favors both straightforward (shallow) and complex architectures (deeper neural networks).<\/jats:p>","DOI":"10.3390\/s21227741","type":"journal-article","created":{"date-parts":[[2021,11,21]],"date-time":"2021-11-21T21:00:50Z","timestamp":1637528450000},"page":"7741","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Random Fourier Features-Based Deep Learning Improvement with Class Activation Interpretability for Nerve Structure Segmentation"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9216-9943","authenticated-orcid":false,"given":"Cristian Alfonso","family":"Jimenez-Casta\u00f1o","sequence":"first","affiliation":[{"name":"Automatic Research Group, Universidad Tecnol\u00f3gica de Pereira, Pereira 660003, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0308-9576","authenticated-orcid":false,"given":"Andr\u00e9s Marino","family":"\u00c1lvarez-Meza","sequence":"additional","affiliation":[{"name":"Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170003, Colombia"}]},{"given":"Oscar David","family":"Aguirre-Ospina","sequence":"additional","affiliation":[{"name":"Medicina Hospitalaria, Servicios Especiales de Salud (SES) Hospital de Caldas, Manizales 170003, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0522-8683","authenticated-orcid":false,"given":"David Augusto","family":"C\u00e1rdenas-Pe\u00f1a","sequence":"additional","affiliation":[{"name":"Automatic Research Group, Universidad Tecnol\u00f3gica de Pereira, Pereira 660003, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1167-1446","authenticated-orcid":false,"given":"\u00c1lvaro Angel","family":"Orozco-Guti\u00e9rrez","sequence":"additional","affiliation":[{"name":"Automatic Research Group, Universidad Tecnol\u00f3gica de Pereira, Pereira 660003, Colombia"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gil Gonz\u00e1lez, J., \u00c1lvarez, A., Valencia, A., and Orozco, A. (2018). Automatic peripheral nerve segmentation in presence of multiple annotators. Iberoamerican Congress on Pattern Recognition, Springer. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).","DOI":"10.1007\/978-3-319-75193-1_30"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Abraham, N., Illanko, K., Khan, N., and Androutsos, D. (2019, January 27\u201329). Deep Learning for Semantic Segmentation of Brachial Plexus Nervesin Ultrasound Images Using U-Net and M-Net. Proceedings of the 2019 3rd International Conference on Imaging, Signal Processing and Communication (ICISPC), Singapore.","DOI":"10.1109\/ICISPC.2019.8935668"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1111\/anae.13921","article-title":"Improving needle tip identification during ultrasound-guided procedures in anaesthetic practice","volume":"72","author":"Scholten","year":"2017","journal-title":"Anaesthesia"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1007\/s11548-018-1721-y","article-title":"Convolution neural networks for real-time needle detection and localization in 2D ultrasound","volume":"13","author":"Mwikirize","year":"2018","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/TMI.2017.2739110","article-title":"Automatic Localization of the Needle Target for Ultrasound-Guided Epidural Injections","volume":"37","author":"Pesteie","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2439","DOI":"10.1016\/j.ultrasmedbio.2020.03.017","article-title":"DeepNerve: A New Convolutional Neural Network for the Localization and Segmentation of the Median Nerve in Ultrasound Image Sequences","volume":"46","author":"Horng","year":"2020","journal-title":"Ultrasound Med. Biol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Campilho, A., Karray, F., and ter Haar Romeny, B. (2018). Nerve Structure Segmentation from Ultrasound Images Using Random Under-Sampling and an SVM Classifier. Image Analysis and Recognition, Springer.","DOI":"10.1007\/978-3-319-93000-8"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gonz\u00c1lez, J.G., \u00c1lvarez, M.A., and Orozco, A.A. (2016, January 16\u201320). A probabilistic framework based on SLIC-superpixel and Gaussian processes for segmenting nerves in ultrasound images. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7591636"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ferr\u00e1ndez Vicente, J.M., \u00c1lvarez-S\u00e1nchez, J.R., de la Paz L\u00f3pez, F., Toledo Moreo, J., and Adeli, H. (2019). HAPAN: Support Tool for Practicing Regional Anesthesia in Peripheral Nerves. Understanding the Brain Function and Emotions, Springer.","DOI":"10.1007\/978-3-030-19591-5"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Giraldo, J.J., \u00c1lvarez, M.A., and Orozco, A.A. (2015, January 25\u201329). Peripheral nerve segmentation using Nonparametric Bayesian Hierarchical Clustering. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7319048"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"241","DOI":"10.5614\/itbj.ict.res.appl.2019.13.3.5","article-title":"Ultrasound nerve segmentation using deep probabilistic programming","volume":"13","author":"Rubasinghe","year":"2019","journal-title":"J. ICT Res. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015). Fully Convolutional Networks for Semantic Segmentation. arXiv.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"20508-1","DOI":"10.2352\/J.ImagingSci.Technol.2020.64.2.020508","article-title":"Medical Image Segmentation based on U-Net: A Review","volume":"64","author":"Du","year":"2020","journal-title":"J. Imaging Sci. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kumar, V., Webb, J.M., Gregory, A., Denis, M., Meixner, D.D., Bayat, M., Whaley, D.H., Fatemi, M., and Alizad, A. (2018). Automated and real-time segmentation of suspicious breast masses using convolutional neural network. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0195816"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., and Duchesne, S. (2017). Clinical Target-Volume Delineation in Prostate Brachytherapy Using Residual Neural Networks. Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2017, Springer.","DOI":"10.1007\/978-3-319-66179-7"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"83002","DOI":"10.1109\/ACCESS.2021.3086530","article-title":"Deep Neural Architectures for Medical Image Semantic Segmentation: Review","volume":"9","author":"Khan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Baby, M., and Jereesh, A. (2017, January 20\u201322). Automatic nerve segmentation of ultrasound images. Proceedings of the 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India.","DOI":"10.1109\/ICECA.2017.8203654"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kakade, A., and Dumbali, J. (2018, January 2\u20133). Identification of nerve in ultrasound images using U-net architecture. Proceedings of the 2018 International Conference on Communication information and Computing Technology (ICCICT), Mumbai, India.","DOI":"10.1109\/ICCICT.2018.8325894"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, R., Shen, H., and Zhou, M. (2019, January 9\u201310). Ultrasound Nerve Segmentation of Brachial Plexus Based on Optimized ResU-Net. Proceedings of the 2019 IEEE International Conference on Imaging Systems and Techniques (IST), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/IST48021.2019.9010317"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"115090","DOI":"10.1016\/j.eswa.2021.115090","article-title":"DSANet: Dilated spatial attention for real-time semantic segmentation in urban street scenes","volume":"183","author":"Elhassan","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.neucom.2021.04.012","article-title":"Semantic segmentation of breast ultrasound image with fuzzy deep learning network and breast anatomy constraints","volume":"450","author":"Huang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sch\u00f6lkopf, B., Smola, A.J., and Bach, F. (2002). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press.","DOI":"10.7551\/mitpress\/4175.001.0001"},{"key":"ref_24","first-page":"1","article-title":"Scaling learning algorithms towards AI","volume":"34","author":"Bengio","year":"2017","journal-title":"Large-Scale Kernel Mach."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.patrec.2018.09.016","article-title":"Convolutional kernel networks based on a convex combination of cosine kernels","volume":"116","author":"Monsefi","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_26","unstructured":"Wilson, A.G., Hu, Z., Salakhutdinov, R., and Xing, E.P. (2015). Deep Kernel Learning. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lee, H., Grosse, R., Ranganath, R., and Ng, A.Y. (2009, January 14\u201318). Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations. Proceedings of the 26th Annual International Conference on Machine Learning (ICML \u201909), Montreal, QC, Canada.","DOI":"10.1145\/1553374.1553453"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2154","DOI":"10.1109\/TMM.2014.2351788","article-title":"Learning High-Level Feature by Deep Belief Networks for 3-D Model Retrieval and Recognition","volume":"16","author":"Bu","year":"2014","journal-title":"IEEE Trans. Multimed."},{"key":"ref_29","first-page":"2627","article-title":"Convolutional kernel networks","volume":"27","author":"Mairal","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Poria, S., Cambria, E., and Gelbukh, A. (2015, January 17\u201321). Deep convolutional neural network textual features and multiple kernel learning for utterance-level multimodal sentiment analysis. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal.","DOI":"10.18653\/v1\/D15-1303"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.neucom.2021.05.092","article-title":"SDCRKL-GP: Scalable deep convolutional random kernel learning in gaussian process for image recognition","volume":"456","author":"Wang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Le, L., Hao, J., Xie, Y., and Priestley, J. (2016, January 6\u20139). Deep Kernel: Learning Kernel Function from Data Using Deep Neural Network. Proceedings of the 2016 IEEE\/ACM 3rd International Conference on Big Data Computing Applications and Technologies (BDCAT), Shanghai, China.","DOI":"10.1145\/3006299.3006312"},{"key":"ref_33","unstructured":"Ober, S.W., Rasmussen, C.E., and van der Wilk, M. (2021). The Promises and Pitfalls of Deep Kernel Learning. arXiv."},{"key":"ref_34","first-page":"5","article-title":"Random Features for Large-Scale Kernel Machines","volume":"3","author":"Rahimi","year":"2007","journal-title":"NIPS"},{"key":"ref_35","unstructured":"Rudin, W. (2017). Fourier Analysis on Groups, Courier Dover Publications."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1007\/s10489-019-01538-w","article-title":"A fast and accurate explicit kernel map","volume":"50","author":"Francis","year":"2020","journal-title":"Appl. Intell."},{"key":"ref_37","unstructured":"Le, Q., Sarl\u00f3s, T., and Smola, A. (2013). Fastfood\u2014Approximating kernel expansions in loglinear time. arXiv."},{"key":"ref_38","first-page":"1975","article-title":"Orthogonal random features","volume":"29","author":"Yu","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_39","unstructured":"Munkhoeva, M., Kapushev, Y., Burnaev, E., and Oseledets, I. (2018). Quadrature-based features for kernel approximation. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1007\/s10462-020-09880-z","article-title":"Major advancements in kernel function approximation","volume":"54","author":"Francis","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1109\/TUFFC.2020.3022324","article-title":"Deep Learning for Automatic Segmentation of Hybrid Optoacoustic Ultrasound (OPUS) Images","volume":"68","author":"Lafci","year":"2021","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control."},{"key":"ref_42","unstructured":"Kaggle (2021, October 05). Ultrasound Nerve Segmentation. Available online: https:\/\/www.kaggle.com\/c\/ultrasound-nerve-segmentation\/data."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chattopadhay, A., Sarkar, A., Howlader, P., and Balasubramanian, V.N. (2018, January 12\u201315). Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00097"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Vinogradova, K., Dibrov, A., and Myers, G. (2020, January 7\u201312). Towards Interpretable Semantic Segmentation via Gradient-Weighted Class Activation Mapping (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i10.7244"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Bengio, Y. (2012). Practical recommendations for gradient-based training of deep architectures. Neural Networks: Tricks of the Trade, Springer.","DOI":"10.1007\/978-3-642-35289-8_26"},{"key":"ref_46","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road Extraction by Deep Residual U-Net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Huang, P.S., Deng, L., Hasegawa-Johnson, M., and He, X. (2013, January 26\u201331). Random features for Kernel Deep Convex Network. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638237"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"\u00c1lvarez-Meza, A.M., C\u00e1rdenas-Pe\u00f1a, D., and Castellanos-Dominguez, G. (2014). Unsupervised kernel function building using maximization of information potential variability. Proceedings of the Iberoamerican Congress on Pattern Recognition, Springer.","DOI":"10.1007\/978-3-319-12568-8_41"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (2016, January 27\u201330). Learning deep features for discriminative localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014). Visualizing and understanding convolutional networks. Proceedings of the European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_53","unstructured":"G\u00e9ron, A. (2019). Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O\u2019Reilly Media."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Gil-Gonz\u00c1lez, J., Valencia-Duque, A., \u00c1lvarez Meza, A., Orozco-Guti\u00e9rrez, A., and Garc\u00eda-Moreno, A. (2021). Regularized Chained Deep Neural Network Classifier for Multiple Annotators. Appl. Sci., 11.","DOI":"10.3390\/app11125409"},{"key":"ref_55","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"103077","DOI":"10.1016\/j.bspc.2021.103077","article-title":"Attention Res-UNet with Guided Decoder for semantic segmentation of brain tumors","volume":"71","author":"Maji","year":"2022","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_57","unstructured":"Yamazaki, K., Rathour, V.S., and Le, T. (2021). Invertible Residual Network with Regularization for Effective Medical Image Segmentation. arXiv."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Banerjee, S., Ling, S.H., Lyu, J., Su, S., and Zheng, Y.P. (2020, January 20\u201324). Automatic segmentation of 3d ultrasound spine curvature using convolutional neural network. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9175673"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/22\/7741\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:33:27Z","timestamp":1760168007000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/22\/7741"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,20]]},"references-count":58,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21227741"],"URL":"https:\/\/doi.org\/10.3390\/s21227741","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,20]]}}}