Detection of adverse events leading to inadvertent injury during laparoscopic cholecystectomy using convolutional neural networks
Author:
Beyersdorffer Patrick1ORCID, Kunert Wolfgang2ORCID, Jansen Kai2, Miller Johanna2, Wilhelm Peter2ORCID, Burgert Oliver1ORCID, Kirschniak Andreas2ORCID, Rolinger Jens2ORCID
Affiliation:
1. Department of Medical Informatics , Reutlingen University , Reutlingen , Germany 2. Department of Surgery and Transplantation , Tübingen University Hospital , Tübingen , Germany
Abstract
Abstract
Uncontrolled movements of laparoscopic instruments can lead to inadvertent injury of adjacent structures. The risk becomes evident when the dissecting instrument is located outside the field of view of the laparoscopic camera. Technical solutions to ensure patient safety are appreciated. The present work evaluated the feasibility of an automated binary classification of laparoscopic image data using Convolutional Neural Networks (CNN) to determine whether the dissecting instrument is located within the laparoscopic image section. A unique record of images was generated from six laparoscopic cholecystectomies in a surgical training environment to configure and train the CNN. By using a temporary version of the neural network, the annotation of the training image files could be automated and accelerated. A combination of oversampling and selective data augmentation was used to enlarge the fully labeled image data set and prevent loss of accuracy due to imbalanced class volumes. Subsequently the same approach was applied to the comprehensive, fully annotated Cholec80 database. The described process led to the generation of extensive and balanced training image data sets. The performance of the CNN-based binary classifiers was evaluated on separate test records from both databases. On our recorded data, an accuracy of 0.88 with regard to the safety-relevant classification was achieved. The subsequent evaluation on the Cholec80 data set yielded an accuracy of 0.84. The presented results demonstrate the feasibility of a binary classification of laparoscopic image data for the detection of adverse events in a surgical training environment using a specifically configured CNN architecture.
Publisher
Walter de Gruyter GmbH
Subject
Biomedical Engineering
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