Cargando…
Real-Time Tool Detection for Workflow Identification in Open Cranial Vault Remodeling
Deep learning is a recent technology that has shown excellent capabilities for recognition and identification tasks. This study applies these techniques in open cranial vault remodeling surgeries performed to correct craniosynostosis. The objective was to automatically recognize surgical tools in re...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8303376/ https://www.ncbi.nlm.nih.gov/pubmed/34206962 http://dx.doi.org/10.3390/e23070817 |
_version_ | 1783727071969673216 |
---|---|
author | Pose Díez de la Lastra, Alicia García-Duarte Sáenz, Lucía García-Mato, David Hernández-Álvarez, Luis Ochandiano, Santiago Pascau, Javier |
author_facet | Pose Díez de la Lastra, Alicia García-Duarte Sáenz, Lucía García-Mato, David Hernández-Álvarez, Luis Ochandiano, Santiago Pascau, Javier |
author_sort | Pose Díez de la Lastra, Alicia |
collection | PubMed |
description | Deep learning is a recent technology that has shown excellent capabilities for recognition and identification tasks. This study applies these techniques in open cranial vault remodeling surgeries performed to correct craniosynostosis. The objective was to automatically recognize surgical tools in real-time and estimate the surgical phase based on those predictions. For this purpose, we implemented, trained, and tested three algorithms based on previously proposed Convolutional Neural Network architectures (VGG16, MobileNetV2, and InceptionV3) and one new architecture with fewer parameters (CranioNet). A novel 3D Slicer module was specifically developed to implement these networks and recognize surgical tools in real time via video streaming. The training and test data were acquired during a surgical simulation using a 3D printed patient-based realistic phantom of an infant’s head. The results showed that CranioNet presents the lowest accuracy for tool recognition (93.4%), while the highest accuracy is achieved by the MobileNetV2 model (99.6%), followed by VGG16 and InceptionV3 (98.8% and 97.2%, respectively). Regarding phase detection, InceptionV3 and VGG16 obtained the best results (94.5% and 94.4%), whereas MobileNetV2 and CranioNet presented worse values (91.1% and 89.8%). Our results prove the feasibility of applying deep learning architectures for real-time tool detection and phase estimation in craniosynostosis surgeries. |
format | Online Article Text |
id | pubmed-8303376 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83033762021-07-25 Real-Time Tool Detection for Workflow Identification in Open Cranial Vault Remodeling Pose Díez de la Lastra, Alicia García-Duarte Sáenz, Lucía García-Mato, David Hernández-Álvarez, Luis Ochandiano, Santiago Pascau, Javier Entropy (Basel) Article Deep learning is a recent technology that has shown excellent capabilities for recognition and identification tasks. This study applies these techniques in open cranial vault remodeling surgeries performed to correct craniosynostosis. The objective was to automatically recognize surgical tools in real-time and estimate the surgical phase based on those predictions. For this purpose, we implemented, trained, and tested three algorithms based on previously proposed Convolutional Neural Network architectures (VGG16, MobileNetV2, and InceptionV3) and one new architecture with fewer parameters (CranioNet). A novel 3D Slicer module was specifically developed to implement these networks and recognize surgical tools in real time via video streaming. The training and test data were acquired during a surgical simulation using a 3D printed patient-based realistic phantom of an infant’s head. The results showed that CranioNet presents the lowest accuracy for tool recognition (93.4%), while the highest accuracy is achieved by the MobileNetV2 model (99.6%), followed by VGG16 and InceptionV3 (98.8% and 97.2%, respectively). Regarding phase detection, InceptionV3 and VGG16 obtained the best results (94.5% and 94.4%), whereas MobileNetV2 and CranioNet presented worse values (91.1% and 89.8%). Our results prove the feasibility of applying deep learning architectures for real-time tool detection and phase estimation in craniosynostosis surgeries. MDPI 2021-06-26 /pmc/articles/PMC8303376/ /pubmed/34206962 http://dx.doi.org/10.3390/e23070817 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Pose Díez de la Lastra, Alicia García-Duarte Sáenz, Lucía García-Mato, David Hernández-Álvarez, Luis Ochandiano, Santiago Pascau, Javier Real-Time Tool Detection for Workflow Identification in Open Cranial Vault Remodeling |
title | Real-Time Tool Detection for Workflow Identification in Open Cranial Vault Remodeling |
title_full | Real-Time Tool Detection for Workflow Identification in Open Cranial Vault Remodeling |
title_fullStr | Real-Time Tool Detection for Workflow Identification in Open Cranial Vault Remodeling |
title_full_unstemmed | Real-Time Tool Detection for Workflow Identification in Open Cranial Vault Remodeling |
title_short | Real-Time Tool Detection for Workflow Identification in Open Cranial Vault Remodeling |
title_sort | real-time tool detection for workflow identification in open cranial vault remodeling |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8303376/ https://www.ncbi.nlm.nih.gov/pubmed/34206962 http://dx.doi.org/10.3390/e23070817 |
work_keys_str_mv | AT posediezdelalastraalicia realtimetooldetectionforworkflowidentificationinopencranialvaultremodeling AT garciaduartesaenzlucia realtimetooldetectionforworkflowidentificationinopencranialvaultremodeling AT garciamatodavid realtimetooldetectionforworkflowidentificationinopencranialvaultremodeling AT hernandezalvarezluis realtimetooldetectionforworkflowidentificationinopencranialvaultremodeling AT ochandianosantiago realtimetooldetectionforworkflowidentificationinopencranialvaultremodeling AT pascaujavier realtimetooldetectionforworkflowidentificationinopencranialvaultremodeling |