The field of surgical computer vision has undergone considerable breakthroughs in recent years with the rising popularity of deep neural network-based methods. However, standard fully-supervised approaches for training such models require vast amounts of annotated data, imposing a prohibitively high cost; especially in the clinical domain. Self-Supervised Learning (SSL) methods, which have begun to gain traction in the general computer vision community, represent a potential solution to these annotation costs, allowing to learn useful representations from only unlabeled data. Still, the effectiveness of SSL methods in more complex and impactful domains, such as medicine and surgery, remains limited and unexplored. In this work, we address this critical need by investigating four state-of-the-art SSL methods (MoCo v2, SimCLR, DINO, SwAV) in the context of surgical computer vision. We present an extensive analysis of the performance of these methods on the Cholec80 dataset for two fundamental and popular tasks in surgical context understanding, phase recognition and tool presence detection. We examine their parameterization, then their behavior with respect to training data quantities in semi-supervised settings. Correct transfer of these methods to surgery, as described and conducted in this work, leads to substantial performance gains over generic uses of SSL - up to 7.4% on phase recognition and 20% on tool presence detection - as well as state-of-the-art semi-supervised phase recognition approaches by up to 14%. Further results obtained on a highly diverse selection of surgical datasets exhibit strong generalization properties. The code is available at https://github.com/CAMMA-public/SelfSupSurg.

Path planning algorithms for steerable catheters, must guarantee anatomical obstacles avoidance, reduce the insertion length and ensure the compliance with needle kinematics. The majority of the solutions in literature focuses on graph based or sampling based methods, both limited by the impossibility to directly obtain smooth trajectories. In this work we formulate the path planning problem as a reinforcement learning problem and show that the trajectory planning model, generated from the training, can provide the user with optimal trajectories in terms of obstacle clearance and kinematic constraints. We obtain 2D and 3D environments from MRI images processing and we implement a GA3C algorithm to create a path planning model, able to generalize on different patients anatomies. The curvilinear trajectories obtained from the model in 2D and 3D environments are compared to the ones obtained by A* and RRT* algorithms. Our method achieves state-of-the-art performances in terms of obstacle avoidance, trajectory smoothness and computational time proving this algorithm as valid planning method for complex environments.