Temporal coherence-based self-supervised learning for laparoscopic workflow analysis

In order to provide the right type of assistance at the right time, computer-assisted surgery systems need context awareness. To achieve this, methods for surgical workflow analysis are crucial. Currently, convolutional neural networks provide the best performance for video-based workflow analysis tasks. For training such networks, large amounts of annotated data are necessary. However, collecting a sufficient amount of data is often costly, time-consuming, and not always feasible. In this paper, we address this problem by presenting and comparing different approaches for self-supervised pretraining of neural networks on unlabeled laparoscopic videos using temporal coherence. We evaluate our pretrained networks on Cholec80, a publicly available dataset for surgical phase segmentation, on which a maximum F1 score of 84.6 was reached. Furthermore, we were able to achieve an increase of the F1 score of up to 10 points when compared to a non-pretrained neural network.Comment: Accepted at the Workshop on Context-Aware Operating Theaters (OR 2.0), a MICCAI satellite even

Optimal Sub-Sequence Matching for the Automatic Prediction of Surgical Tasks

International audienceSurgery is one of the riskiest and most important medical acts that is performed today. The desires to improve patient outcomes, surgeon training, and also to reduce the costs of surgery, have motivated surgeons to equip their Operating Rooms with sensors that describe the surgical intervention. The richness and complexity of the data that is collected calls for new machine learning methods to support pre-, peri- and post-surgery (before, during and after). This paper introduces a new method for the prediction of the next task that the surgeon is going to perform during the surgery (peri). Our method bases its prediction on the optimal matching of the current surgery to a set of pre-recorded surgeries. We assess our method on a set of neurosurgeries (lumbar disc herniation removal) and show that our method outperforms the state of the art by providing a prediction (of the next task that is going to be performed by the surgeon) more than 85% of the time with a 95% accurac

Sequential surgical signatures in micro-suturing task

International audiencePurpose: Surgical processes are generally only studied by identifying differences in populations such as participants or level of expertise. But the similarity between this population is also important in understanding the process. We therefore proposed to study these two aspects. Methods: In this article, we show how similarities in process workflow within a population can be identified as sequential surgical signatures. To this purpose, we have proposed a pattern mining approach to identify these signatures.Validation: We validated our method with a data set composed of seventeen micro-surgical suturing tasks performed by four participants with two levels of expertise.Results: We identified sequential surgical signatures specific to each participant , shared between participants with and without the same level of expertise. These signatures are also able to perfectly define the level of expertise of the participant who performed a new micro-surgical suturing task. However, it is more complicated to determine who the participant is, and the method correctly determines this information in only 64% of cases.Conclusion: We show for the first time the concept of sequential surgical signature. This new concept has the potential to further help to understand surgical procedures and provide useful knowledge to define future CAS systems