Data-Driven Visual Tracking in Retinal Microsurgery

In the context of retinal microsurgery, visual tracking of instruments is a key component of robotics assistance. The difficulty of the task and major reason why most existing strategies fail on {\it in-vivo} image sequences lies in the fact that complex and severe changes in instrument appearance are challenging to model. This paper introduces a novel approach, that is both data-driven and complementary to existing tracking techniques. In particular, we show how to learn and integrate an accurate detector with a simple gradient-based tracker within a robust pipeline which runs at framerate. In addition, we present a fully annotated dataset of retinal instruments in {\it in-vivo} surgeries, which we use to quantitatively validate our approach. We also demonstrate an application of our method in a laparoscopy image sequence

Cirurgia assistida mitjançant visió per computador

En aquest projecte s'ha desenvolupat una solució informàtica que, a partir de l'enregistrament en vídeo d'una cirurgia mínimament invasiva, és capaç de detectar i marcar sobre aquest vídeo les eines utilitzades per el cirurgià.In this project an informatic solution has been developed which, given a recording video of a minimal invasive surgery, it is able to detect and point on this video the surgical tools that has been used

Visual Tracking in Robotic Minimally Invasive Surgery

Intra-operative imaging and robotics are some of the technologies driving forward better and more effective minimally invasive surgical procedures. To advance surgical practice and capabilities further, one of the key requirements for computationally enhanced interventions is to know how instruments and tissues move during the operation. While endoscopic video captures motion, the complex appearance dynamic effects of surgical scenes are challenging for computer vision algorithms to handle with robustness. Tackling both tissue and instrument motion estimation, this thesis proposes a combined non-rigid surface deformation estimation method to track tissue surfaces robustly and in conditions with poor illumination. For instrument tracking, a keypoint based 2D tracker that relies on the Generalized Hough Transform is developed to initialize a 3D tracker in order to robustly track surgical instruments through long sequences that contain complex motions. To handle appearance changes and occlusion a patch-based adaptive weighting with segmentation and scale tracking framework is developed. It takes a tracking-by-detection approach and a segmentation model is used to assigns weights to template patches in order to suppress back- ground information. The performance of the method is thoroughly evaluated showing that without any offline-training, the tracker works well even in complex environments. Finally, the thesis proposes a novel 2D articulated instrument pose estimation framework, which includes detection-regression fully convolutional network and a multiple instrument parsing component. The framework achieves compelling performance and illustrates interesting properties includ- ing transfer between different instrument types and between ex vivo and in vivo data. In summary, the thesis advances the state-of-the art in visual tracking for surgical applications for both tissue and instrument motion estimation. It contributes to developing the technological capability of full surgical scene understanding from endoscopic video

Augmentation Of Human Skill In Microsurgery

Surgeons performing highly skilled microsurgery tasks can benefit from information and manual assistance to overcome technological and physiological limitations to make surgery safer, efficient, and more successful. Vitreoretinal surgery is particularly difficult due to inherent micro-scale and fragility of human eye anatomy. Additionally, surgeons are challenged by physiological hand tremor, poor visualization, lack of force sensing, and significant cognitive load while executing high-risk procedures inside the eye, such as epiretinal membrane peeling. This dissertation presents the architecture and the design principles for a surgical augmentation environment which is used to develop innovative functionality to address the fundamental limitations in vitreoretinal surgery. It is an inherently information driven modular system incorporating robotics, sensors, and multimedia components. The integrated nature of the system is leveraged to create intuitive and relevant human-machine interfaces and generate a particular system behavior to provide active physical assistance and present relevant sensory information to the surgeon. These include basic manipulation assistance, audio-visual and haptic feedback, intraoperative imaging and force sensing. The resulting functionality, and the proposed architecture and design methods generalize to other microsurgical procedures. The system's performance is demonstrated and evaluated using phantoms and in vivo experiments

Unified Detection and Tracking in Retinal Microsurgery

Abstract. Traditionally, tool tracking involves two subtasks: (i) detecting the tool in the initial image in which it appears, and (ii) predicting and refining the configuration of the detected tool in subsequent images. With retinal microsurgery in mind, we propose a unified tool detection and tracking framework, removing the need for two separate systems. The basis of our approach is to treat both detection and tracking as a sequential entropy minimization problem, where the goal is to determine the parameters describing a surgical tool in each frame. The resulting framework is capable of both detecting and tracking in situations where the tool enters and leaves the field of view regularly. We demonstrate the benefits of this method in the context of retinal tool tracking. Through extensive experimentation on a phantom eye, we show that this method provides efficient and robust tool tracking and detection