1,126 research outputs found
Automatic alignment of surgical videos using kinematic data
Over the past one hundred years, the classic teaching methodology of "see
one, do one, teach one" has governed the surgical education systems worldwide.
With the advent of Operation Room 2.0, recording video, kinematic and many
other types of data during the surgery became an easy task, thus allowing
artificial intelligence systems to be deployed and used in surgical and medical
practice. Recently, surgical videos has been shown to provide a structure for
peer coaching enabling novice trainees to learn from experienced surgeons by
replaying those videos. However, the high inter-operator variability in
surgical gesture duration and execution renders learning from comparing novice
to expert surgical videos a very difficult task. In this paper, we propose a
novel technique to align multiple videos based on the alignment of their
corresponding kinematic multivariate time series data. By leveraging the
Dynamic Time Warping measure, our algorithm synchronizes a set of videos in
order to show the same gesture being performed at different speed. We believe
that the proposed approach is a valuable addition to the existing learning
tools for surgery.Comment: Accepted at AIME 201
Gesture Recognition in Robotic Surgery: a Review
OBJECTIVE: Surgical activity recognition is a fundamental step in computer-assisted interventions. This paper reviews the state-of-the-art in methods for automatic recognition of fine-grained gestures in robotic surgery focusing on recent data-driven approaches and outlines the open questions and future research directions. METHODS: An article search was performed on 5 bibliographic databases with combinations of the following search terms: robotic, robot-assisted, JIGSAWS, surgery, surgical, gesture, fine-grained, surgeme, action, trajectory, segmentation, recognition, parsing. Selected articles were classified based on the level of supervision required for training and divided into different groups representing major frameworks for time series analysis and data modelling. RESULTS: A total of 52 articles were reviewed. The research field is showing rapid expansion, with the majority of articles published in the last 4 years. Deep-learning-based temporal models with discriminative feature extraction and multi-modal data integration have demonstrated promising results on small surgical datasets. Currently, unsupervised methods perform significantly less well than the supervised approaches. CONCLUSION: The development of large and diverse open-source datasets of annotated demonstrations is essential for development and validation of robust solutions for surgical gesture recognition. While new strategies for discriminative feature extraction and knowledge transfer, or unsupervised and semi-supervised approaches, can mitigate the need for data and labels, they have not yet been demonstrated to achieve comparable performance. Important future research directions include detection and forecast of gesture-specific errors and anomalies. SIGNIFICANCE: This paper is a comprehensive and structured analysis of surgical gesture recognition methods aiming to summarize the status of this rapidly evolving field
Deep learning for time series classification
Time series analysis is a field of data science which is interested in
analyzing sequences of numerical values ordered in time. Time series are
particularly interesting because they allow us to visualize and understand the
evolution of a process over time. Their analysis can reveal trends,
relationships and similarities across the data. There exists numerous fields
containing data in the form of time series: health care (electrocardiogram,
blood sugar, etc.), activity recognition, remote sensing, finance (stock market
price), industry (sensors), etc. Time series classification consists of
constructing algorithms dedicated to automatically label time series data. The
sequential aspect of time series data requires the development of algorithms
that are able to harness this temporal property, thus making the existing
off-the-shelf machine learning models for traditional tabular data suboptimal
for solving the underlying task. In this context, deep learning has emerged in
recent years as one of the most effective methods for tackling the supervised
classification task, particularly in the field of computer vision. The main
objective of this thesis was to study and develop deep neural networks
specifically constructed for the classification of time series data. We thus
carried out the first large scale experimental study allowing us to compare the
existing deep methods and to position them compared other non-deep learning
based state-of-the-art methods. Subsequently, we made numerous contributions in
this area, notably in the context of transfer learning, data augmentation,
ensembling and adversarial attacks. Finally, we have also proposed a novel
architecture, based on the famous Inception network (Google), which ranks among
the most efficient to date.Comment: PhD thesi
Unsupervised Trajectory Segmentation for Surgical Gesture Recognition in Robotic Training
International audienceDexterity and procedural knowledge are two critical skills that surgeons need to master to perform accurate and safe surgical interventions. However, current training systems do not allow us to provide an in-depth analysis of surgical gestures to precisely assess these skills. Our objective is to develop a method for the automatic and quantitative assessment of surgical gestures. To reach this goal, we propose a new unsupervised algorithm that can automatically segment kinematic data from robotic training sessions. Without relying on any prior information or model, this algorithm detects critical points in the kinematic data that define relevant spatio-temporal segments. Based on the association of these segments, we obtain an accurate recognition of the gestures involved in the surgical training task. We, then, perform an advanced analysis and assess our algorithm using datasets recorded during real expert training sessions. After comparing our approach with the manual annotations of the surgical gestures, we observe 97.4% accuracy for the learning purpose and an average matching score of 81.9% for the fully automated gesture recognition process. Our results show that trainees workflow can be followed and surgical gestures may be automatically evaluated according to an expert database. This approach tends toward improving training efficiency by minimizing the learning curve
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