Neural Network Classification of Surgical Tools in Gynecological Videos
Abstract: Automated surgical tool classification will improve the workflow of surgery. Previous research tackled this task mainly in cholecystectomy procedures due to availability of a relatively large and labelled set (Cholec80 dataset). However, the complexity of the procedure type has an impact on the robustness of the deep learning approaches. Therefore, the classification capability of CNNs on data of more complex procedures with many surgical tools was investigated. In this work, laparoscopic videos of 14 gynaecological procedures were recorded and labelled for surgical tool presence. Then, the DenseNet-121 model was trained to identify surgical tools according to functionality. Experimental results imply high classification performance for some surgical tools. The mean average precision over all the tools was 67%. This study is an initial benchmark for detecting surgical tools in realistic settings.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Neural Network Classification of Surgical Tools in Gynecological Videos ; volume:8 ; number:2 ; year:2022 ; pages:644-647 ; extent:4
Current directions in biomedical engineering ; 8, Heft 2 (2022), 644-647 (gesamt 4)
- Creator
- DOI
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10.1515/cdbme-2022-1164
- URN
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urn:nbn:de:101:1-2022090315260502683766
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:30 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
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Robustness of Convolutional Neural Networks for Surgical Tool Classification in Laparoscopic Videos from Multiple Sources and of Multiple Types: A Systematic Evaluation
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Data Recording Framework for Physiological and Surgical Data in Operating Theatres
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Spatial-temporal Modelling for Surgical Tool Classification in Cholecystectomy Videos
The Image flip effect on a CNN model classification
Fusing CNN features to improve generalisability for surgical tool classification
Analysis of expansion within a pressure inflated section of an urethral stricture model
Using adaptive learning rate to generate adversarial images
Robustness evaluation on different training state of a CNN model
Robustness of Convolutional Neural Networks for Surgical Tool Classification in Laparoscopic Videos from Multiple Sources and of Multiple Types: A Systematic Evaluation
Assessing Generalisation Capabilities of CNN Models for Surgical Tool Classification
Predicting Surgical Phases using CNN-NARX Neural Network
Data Recording Framework for Physiological and Surgical Data in Operating Theatres
Traditional versus Neural Network Classification Methods for Facial Emotion Recognition
Surgical Tool Classification in Laparoscopic Videos Using Convolutional Neural Network
Spatial-temporal Modelling for Surgical Tool Classification in Cholecystectomy Videos
The Image flip effect on a CNN model classification
Fusing CNN features to improve generalisability for surgical tool classification
Analysis of expansion within a pressure inflated section of an urethral stricture model
Using adaptive learning rate to generate adversarial images
Robustness evaluation on different training state of a CNN model
Robustness of Convolutional Neural Networks for Surgical Tool Classification in Laparoscopic Videos from Multiple Sources and of Multiple Types: A Systematic Evaluation
Assessing Generalisation Capabilities of CNN Models for Surgical Tool Classification
Predicting Surgical Phases using CNN-NARX Neural Network
Data Recording Framework for Physiological and Surgical Data in Operating Theatres