P-CSEM: An Attention Module for Improved Laparoscopic Surgical Tool Detection
Author:
Arabian Herag1ORCID, Abdulbaki Alshirbaji Tamer12ORCID, Jalal Nour Aldeen12ORCID, Krueger-Ziolek Sabine1, Moeller Knut134ORCID
Affiliation:
1. Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany 2. Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, 04103 Leipzig, Germany 3. Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand 4. Department of Microsystems Engineering, University of Freiburg, 79110 Freiburg, Germany
Abstract
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.
Funder
German Federal Ministry of Research and Education Ministerium für Wissenschaft, Forschung und Kunst (MWK) of Baden-Wuerttemberg, Germany
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
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