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P-CSEM: An Attention Module for Improved Laparoscopic Surgical Tool Detection
Minimal invasive surgery, more specifically laparoscopic surgery, is an active topic in the field of research. The collaboration between surgeons and new technologies aims to improve operation procedures as well as to ensure the safety of patients. An integral part of operating rooms modernization i...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10459566/ https://www.ncbi.nlm.nih.gov/pubmed/37631791 http://dx.doi.org/10.3390/s23167257 |
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author | Arabian, Herag Abdulbaki Alshirbaji, Tamer Jalal, Nour Aldeen Krueger-Ziolek, Sabine Moeller, Knut |
author_facet | Arabian, Herag Abdulbaki Alshirbaji, Tamer Jalal, Nour Aldeen Krueger-Ziolek, Sabine Moeller, Knut |
author_sort | Arabian, Herag |
collection | PubMed |
description | Minimal invasive surgery, more specifically laparoscopic surgery, is an active topic in the field of research. The collaboration between surgeons and new technologies aims to improve operation procedures as well as to ensure the safety of patients. An integral part of operating rooms modernization is the real-time communication between the surgeon and the data gathered using the numerous devices during surgery. A fundamental tool that can aid surgeons during laparoscopic surgery is the recognition of the different phases during an operation. Current research has shown a correlation between the surgical tools utilized and the present phase of surgery. To this end, a robust surgical tool classifier is desired for optimal performance. In this paper, a deep learning framework embedded with a custom attention module, the P-CSEM, has been proposed to refine the spatial features for surgical tool classification in laparoscopic surgery videos. This approach utilizes convolutional neural networks (CNNs) integrated with P-CSEM attention modules at different levels of the architecture for improved feature refinement. The model was trained and tested on the popular, publicly available Cholec80 database. Results showed that the attention integrated model achieved a mean average precision of 93.14%, and visualizations revealed the ability of the model to adhere more towards features of tool relevance. The proposed approach displays the benefits of integrating attention modules into surgical tool classification models for a more robust and precise detection. |
format | Online Article Text |
id | pubmed-10459566 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104595662023-08-27 P-CSEM: An Attention Module for Improved Laparoscopic Surgical Tool Detection Arabian, Herag Abdulbaki Alshirbaji, Tamer Jalal, Nour Aldeen Krueger-Ziolek, Sabine Moeller, Knut Sensors (Basel) Article Minimal invasive surgery, more specifically laparoscopic surgery, is an active topic in the field of research. The collaboration between surgeons and new technologies aims to improve operation procedures as well as to ensure the safety of patients. An integral part of operating rooms modernization is the real-time communication between the surgeon and the data gathered using the numerous devices during surgery. A fundamental tool that can aid surgeons during laparoscopic surgery is the recognition of the different phases during an operation. Current research has shown a correlation between the surgical tools utilized and the present phase of surgery. To this end, a robust surgical tool classifier is desired for optimal performance. In this paper, a deep learning framework embedded with a custom attention module, the P-CSEM, has been proposed to refine the spatial features for surgical tool classification in laparoscopic surgery videos. This approach utilizes convolutional neural networks (CNNs) integrated with P-CSEM attention modules at different levels of the architecture for improved feature refinement. The model was trained and tested on the popular, publicly available Cholec80 database. Results showed that the attention integrated model achieved a mean average precision of 93.14%, and visualizations revealed the ability of the model to adhere more towards features of tool relevance. The proposed approach displays the benefits of integrating attention modules into surgical tool classification models for a more robust and precise detection. MDPI 2023-08-18 /pmc/articles/PMC10459566/ /pubmed/37631791 http://dx.doi.org/10.3390/s23167257 Text en © 2023 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 Arabian, Herag Abdulbaki Alshirbaji, Tamer Jalal, Nour Aldeen Krueger-Ziolek, Sabine Moeller, Knut P-CSEM: An Attention Module for Improved Laparoscopic Surgical Tool Detection |
title | P-CSEM: An Attention Module for Improved Laparoscopic Surgical Tool Detection |
title_full | P-CSEM: An Attention Module for Improved Laparoscopic Surgical Tool Detection |
title_fullStr | P-CSEM: An Attention Module for Improved Laparoscopic Surgical Tool Detection |
title_full_unstemmed | P-CSEM: An Attention Module for Improved Laparoscopic Surgical Tool Detection |
title_short | P-CSEM: An Attention Module for Improved Laparoscopic Surgical Tool Detection |
title_sort | p-csem: an attention module for improved laparoscopic surgical tool detection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10459566/ https://www.ncbi.nlm.nih.gov/pubmed/37631791 http://dx.doi.org/10.3390/s23167257 |
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