Real-time 3D Tracking of Articulated Tools for Robotic Surgery

In robotic surgery, tool tracking is important for providing safe tool-tissue interaction and facilitating surgical skills assessment. Despite recent advances in tool tracking, existing approaches are faced with major difficulties in real-time tracking of articulated tools. Most algorithms are tailored for offline processing with pre-recorded videos. In this paper, we propose a real-time 3D tracking method for articulated tools in robotic surgery. The proposed method is based on the CAD model of the tools as well as robot kinematics to generate online part-based templates for efficient 2D matching and 3D pose estimation. A robust verification approach is incorporated to reject outliers in 2D detections, which is then followed by fusing inliers with robot kinematic readings for 3D pose estimation of the tool. The proposed method has been validated with phantom data, as well as ex vivo and in vivo experiments. The results derived clearly demonstrate the performance advantage of the proposed method when compared to the state-of-the-art.Comment: This paper was presented in MICCAI 2016 conference, and a DOI was linked to the publisher's versio

Concurrent Segmentation and Localization for Tracking of Surgical Instruments

Real-time instrument tracking is a crucial requirement for various computer-assisted interventions. In order to overcome problems such as specular reflections and motion blur, we propose a novel method that takes advantage of the interdependency between localization and segmentation of the surgical tool. In particular, we reformulate the 2D instrument pose estimation as heatmap regression and thereby enable a concurrent, robust and near real-time regression of both tasks via deep learning. As demonstrated by our experimental results, this modeling leads to a significantly improved performance than directly regressing the tool position and allows our method to outperform the state of the art on a Retinal Microsurgery benchmark and the MICCAI EndoVis Challenge 2015.Comment: I. Laina and N. Rieke contributed equally to this work. Accepted to MICCAI 201

Simultaneous recognition and pose estimation of instruments in minimally invasive surgery

Detection of surgical instruments plays a key role in ensuring patient safety in minimally invasive surgery. In this paper, we present a novel method for 2D vision-based recognition and pose estimation of surgical instruments that generalizes to different surgical applications. At its core, we propose a novel scene model in order to simultaneously recognize multiple instruments as well as their parts. We use a Convolutional Neural Network architecture to embody our model and show that the cross-entropy loss is well suited to optimize its parameters which can be trained in an end-to-end fashion. An additional advantage of our approach is that instrument detection at test time is achieved while avoiding the need for scale-dependent sliding window evaluation. This allows our approach to be relatively parameter free at test time and shows good performance for both instrument detection and tracking. We show that our approach surpasses state-of-the-art results on in-vivo retinal microsurgery image data, as well as ex-vivo laparoscopic sequences

Can surgical simulation be used to train detection and classification of neural networks?

Computer-assisted interventions (CAI) aim to increase the effectiveness, precision and repeatability of procedures to improve surgical outcomes. The presence and motion of surgical tools is a key information input for CAI surgical phase recognition algorithms. Vision-based tool detection and recognition approaches are an attractive solution and can be designed to take advantage of the powerful deep learning paradigm that is rapidly advancing image recognition and classification. The challenge for such algorithms is the availability and quality of labelled data used for training. In this Letter, surgical simulation is used to train tool detection and segmentation based on deep convolutional neural networks and generative adversarial networks. The authors experiment with two network architectures for image segmentation in tool classes commonly encountered during cataract surgery. A commercially-available simulator is used to create a simulated cataract dataset for training models prior to performing transfer learning on real surgical data. To the best of authors' knowledge, this is the first attempt to train deep learning models for surgical instrument detection on simulated data while demonstrating promising results to generalise on real data. Results indicate that simulated data does have some potential for training advanced classification methods for CAI systems

Articulated Multi-Instrument 2D Pose Estimation Using Fully Convolutional Networks

Instrument detection, pose estimation and tracking in surgical videos is an important vision component for computer assisted interventions. While significant advances have been made in recent years, articulation detection is still a major challenge. In this paper, we propose a deep neural network for articulated multi-instrument 2D pose estimation, which is trained on a detailed annotations of endoscopic and microscopic datasets. Our model is formed by a fully convolutional detection-regression network. Joints and associations between joint pairs in our instrument model are located by the detection subnetwork and are subsequently refined through a regression subnetwork. Based on the output from the model, the poses of the instruments are inferred using maximum bipartite graph matching. Our estimation framework is powered by deep learning techniques without any direct kinematic information from a robot. Our framework is tested on single-instrument RMIT data, and also on multi-instrument EndoVis and in vivo data with promising results. In addition, the dataset annotations are publicly released along with our code and model

Surgical tool tracking and pose estimation in retinal microsurgery

Retinal Microsurgery (RM) is performed with small surgical tools which are observed through a microscope. Real-time estimation of the tool’s pose enables the application of various computer-assisted techniques such as augmented reality, with the potential of improving the clinical outcome. However, most existing methods are prone to fail in in-vivo sequences due to partial occlusions, illumination and appearance changes of the tool. To overcome these problems, we propose an algorithm for simultaneous tool tracking and pose estimation that is inspired by state-of-the-art computer vision techniques. Specifically, we introduce a method based on regression forests to track the tool tip and to recover the tool’s articulated pose. To demonstrate the performance of our algorithm, we evaluate on a dataset which comprises four real surgery sequences, and compare with the state-of-the-art methods on a publicly available dataset

Characterising Exoplanet Atmospheres using Traditional Methods and Supervised Machine Learning

Since the discovery of the first extrasolar planets over 25 years ago, the field of exoplanet research has exploded. Today we have over 4000 confirmed exoplanets, with a wide variety of sizes, orbital separations, and host stars. The characterisation of this diverse population of objects has led to exciting discoveries about the conditions of alien worlds. Future technological advances are expected to provide an abundance of exoplanet spectra with a higher precision and sensitivity than ever before. This calls for a parallel advancement in the accuracy and speed of atmospheric models to interpret this influx of data. In this thesis, my work on atmospheric retrievals is presented. Starting with traditional techniques, my first thesis paper applies a Bayesian retrieval in combination with an analytical atmospheric model to the Hubble transmission spectra of 38 different exoplanets. My second paper considers the theoretical model of the sodium doublet, and the effect of dropping the assumption of local-thermodynamic equilibrium. From here, I went on to develop a method that uses supervised machine learning to improve the speed and efficiency of the retrieval. This method was explained and tested in a collaborative paper with machine learning experts in Bern. The machine learning retrieval is then applied in several follow-up studies, covering a range of different scenarios. One of these was my final thesis paper, which further extends the new retrieval to high-resolution spectra using the cross-correlation function. In addition to my own papers, I have contributed to a number of studies led by collaborators by running retrievals, assisting other students, and participating in scientific discussions. I have also worked on several observing proposals, both for high-resolution ground-based observatories and for the upcoming James Webb Space Telescope. I plan to continue my work on exoplanet characterisation and machine learning in the future, using the technique to combine high- and low-resolution spectra to gain further insight into the atmospheres of these distant planets. The speed and efficiency of machine learning will also allow for statistical studies of exoplanets as the quantity of atmospheric spectra from new and upcoming telescopes escalates. Not only will these studies teach us about the conditions and potential habitability of exoplanets, but they will also answer questions about planet formation, diverse chemical processes, and the uniqueness of our solar system

Visual Tracking of Instruments in Minimally Invasive Surgery

Reducing access trauma has been a focal point for modern surgery and tackling the challenges that arise from new operating techniques and instruments is an exciting and open area of research. Lack of awareness and control from indirect manipulation and visualization has created a need to augment the surgeon's understanding and perception of how their instruments interact with the patient's anatomy but current methods of achieving this are inaccurate and difficult to integrate into the surgical workflow. Visual methods have the potential to recover the position and orientation of the instruments directly in the reference frame of the observing camera without the need to introduce additional hardware to the operating room and perform complex calibration steps. This thesis explores how this problem can be solved with the fusion of coarse region and fine scale point features to enable the recovery of both the rigid and articulated degrees of freedom of laparoscopic and robotic instruments using only images provided by the surgical camera. Extensive experiments on different image features are used to determine suitable representations for reliable and robust pose estimation. Using this information a novel framework is presented which estimates 3D pose with a region matching scheme while using frame-to-frame optical flow to account for challenges due to symmetry in the instrument design. The kinematic structure of articulated robotic instruments is also used to track the movement of the head and claspers. The robustness of this method was evaluated on calibrated ex-vivo images and in-vivo sequences and comparative studies are performed with state-of-the-art kinematic assisted tracking methods