Random Fourier Features-Based Deep Learning Improvement with Class Activation Interpretability for Nerve Structure Segmentation

Peripheral nerve blocking (PNB) is a standard procedure to support regional anesthesia. Still, correct localization of the nerve’s 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 lea...

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Autores principales:	Cristian Alfonso Jimenez-Castaño, Andrés Marino Álvarez-Meza, Oscar David Aguirre-Ospina, David Augusto Cárdenas-Peña, Álvaro Angel Orozco-Gutiérrez
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	nerve structure segmentation ultrasound images deep learning random Fourier features class activation mapping Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/6a121e7284ca48b89d1884eee2c25d78
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spelling	oai:doaj.org-article:6a121e7284ca48b89d1884eee2c25d782021-11-25T18:58:56ZRandom Fourier Features-Based Deep Learning Improvement with Class Activation Interpretability for Nerve Structure Segmentation10.3390/s212277411424-8220https://doaj.org/article/6a121e7284ca48b89d1884eee2c25d782021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7741https://doaj.org/toc/1424-8220Peripheral nerve blocking (PNB) is a standard procedure to support regional anesthesia. Still, correct localization of the nerve’s 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).Cristian Alfonso Jimenez-CastañoAndrés Marino Álvarez-MezaOscar David Aguirre-OspinaDavid Augusto Cárdenas-PeñaÁlvaro Angel Orozco-GutiérrezMDPI AGarticlenerve structure segmentationultrasound imagesdeep learningrandom Fourier featuresclass activation mappingChemical technologyTP1-1185ENSensors, Vol 21, Iss 7741, p 7741 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	nerve structure segmentation ultrasound images deep learning random Fourier features class activation mapping Chemical technology TP1-1185
spellingShingle	nerve structure segmentation ultrasound images deep learning random Fourier features class activation mapping Chemical technology TP1-1185 Cristian Alfonso Jimenez-Castaño Andrés Marino Álvarez-Meza Oscar David Aguirre-Ospina David Augusto Cárdenas-Peña Álvaro Angel Orozco-Gutiérrez Random Fourier Features-Based Deep Learning Improvement with Class Activation Interpretability for Nerve Structure Segmentation
description	Peripheral nerve blocking (PNB) is a standard procedure to support regional anesthesia. Still, correct localization of the nerve’s 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).
format	article
author	Cristian Alfonso Jimenez-Castaño Andrés Marino Álvarez-Meza Oscar David Aguirre-Ospina David Augusto Cárdenas-Peña Álvaro Angel Orozco-Gutiérrez
author_facet	Cristian Alfonso Jimenez-Castaño Andrés Marino Álvarez-Meza Oscar David Aguirre-Ospina David Augusto Cárdenas-Peña Álvaro Angel Orozco-Gutiérrez
author_sort	Cristian Alfonso Jimenez-Castaño
title	Random Fourier Features-Based Deep Learning Improvement with Class Activation Interpretability for Nerve Structure Segmentation
title_short	Random Fourier Features-Based Deep Learning Improvement with Class Activation Interpretability for Nerve Structure Segmentation
title_full	Random Fourier Features-Based Deep Learning Improvement with Class Activation Interpretability for Nerve Structure Segmentation
title_fullStr	Random Fourier Features-Based Deep Learning Improvement with Class Activation Interpretability for Nerve Structure Segmentation
title_full_unstemmed	Random Fourier Features-Based Deep Learning Improvement with Class Activation Interpretability for Nerve Structure Segmentation
title_sort	random fourier features-based deep learning improvement with class activation interpretability for nerve structure segmentation
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6a121e7284ca48b89d1884eee2c25d78
work_keys_str_mv	AT cristianalfonsojimenezcastano randomfourierfeaturesbaseddeeplearningimprovementwithclassactivationinterpretabilityfornervestructuresegmentation AT andresmarinoalvarezmeza randomfourierfeaturesbaseddeeplearningimprovementwithclassactivationinterpretabilityfornervestructuresegmentation AT oscardavidaguirreospina randomfourierfeaturesbaseddeeplearningimprovementwithclassactivationinterpretabilityfornervestructuresegmentation AT davidaugustocardenaspena randomfourierfeaturesbaseddeeplearningimprovementwithclassactivationinterpretabilityfornervestructuresegmentation AT alvaroangelorozcogutierrez randomfourierfeaturesbaseddeeplearningimprovementwithclassactivationinterpretabilityfornervestructuresegmentation
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Random Fourier Features-Based Deep Learning Improvement with Class Activation Interpretability for Nerve Structure Segmentation

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