Danail Stoyanov

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeons navigation capabilities by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D optical imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions.

Soft-tissue motion tracking and structure estimation for robotic assisted MIS procedures

In robotically assisted laparoscopic surgery, soft-tissue motion tracking and structure recovery are important for intraoperative surgical guidance, motion compensation and delivering active constraints. In this paper, we present a novel method for feature based motion tracking of deformable soft-tissue surfaces in totally endoscopic coronary artery bypass graft (TECAB) surgery. We combine two feature detectors to recover distinct regions on the epicardial surface for which the sparse 3D surface geometry may be computed using a pre-calibrated stereo laparoscope. The movement of the 3D points is then tracked in the stereo images with stereo-temporal constrains by using an iterative registration algorithm. The practical value of the technique is demonstrated on both a deformable phantom model with tomographically derived surface geometry and in vivo robotic assisted minimally invasive surgery (MIS) image sequences.

Real-time stereo reconstruction in robotically assisted minimally invasive surgery

The recovery of 3D tissue structure and morphology during robotic assisted surgery is an important step towards accurate deployment of surgical guidance and control techniques in minimally invasive therapies. In this article, we present a novel stereo reconstruction algorithm that propagates disparity information around a set of candidate feature matches. This has the advantage of avoiding problems with specular highlights, occlusions from instruments and view dependent illumination bias. Furthermore, the algorithm can be used with any feature matching strategy allowing the propagation of depth in very disparate views. Validation is provided for a phantom model with known geometry and this data is available online in order to establish a structured validation scheme in the field. The practical value of the proposed method is further demonstrated by reconstructions on various in vivo images of robotic assisted procedures, which are also available to the community.

Three-Dimensional Tissue Deformation Recovery and Tracking

Recent advances in surgical robotics have provided a platform for extending the current capabilities of minimally invasive surgery by incorporating both preoperative and intraoperative imaging data. In this tutorial article, we introduce techniques for in vivo three-dimensional (3-D) tissue deformation recovery and tracking based on laparoscopic or endoscopic images. These optically based techniques provide a unique opportunity for recovering surface deformation of the soft tissue without the need of additional instrumentation. They can therefore be easily incorporated into the existing surgical workflow. Technically, the problem formulation is challenging due to nonrigid deformation of the tissue and instrument interaction. Current approaches and future research directions in terms of intraoperative planning and adaptive surgical navigation are explained in detail.

A practical approach towards accurate dense 3D depth recovery for robotic laparoscopic surgery

Objective: Recovering tissue deformation during robotic-assisted minimally invasive surgery (MIS) is an important step towards motion compensation and stabilization. This article presents a practical strategy for dense 3D depth recovery and temporal motion tracking for deformable surfaces. Methods: The method combines image rectification with constrained disparity registration for reliable depth estimation. The accuracy and practical value of the technique are validated using a tissue phantom with known 3D geometry and motion characteristics and in vivo data. Results: Results from the phantom model correctly follow the motion trend indicated from the ground truth provided by CT scanning, and regression analysis shows the intrinsic accuracy that can be achieved with the proposed technique. Results applied to in vivo robotic-assisted MIS data are also provided, indicating the practical value of the proposed method. Conclusion: The proposed method presents a practical strategy for dense depth recovery of surface structure in robotic-assisted MIS that incorporates stereo vision. Results on phantom and in vivo data indicate the quality of the method and also highlight the importance of further considering the effects of specular highlights.

Simultaneous stereoscope localization and soft-tissue mapping for minimal invasive surgery

Minimally Invasive Surgery (MIS) has recognized benefits of reduced patient trauma and recovery time. In practice, MIS procedures present a number of challenges due to the loss of 3D vision and the narrow field-of-view provided by the camera. The restricted vision can make navigation and localization within the human body a challenging task. This paper presents a robust technique for building a repeatable long term 3D map of the scene whilst recovering the camera movement based on Simultaneous Localization and Mapping (SLAM). A sequential vision only approach is adopted which provides 6 DOF camera movement that exploits the available textured surfaces and reduces reliance on strong planar structures required for range finders. The method has been validated with a simulated data set using real MIS textures, as well as in vivo MIS video sequences. The results indicate the strength of the proposed algorithm under the complex reflectance properties of the scene, and the potential for real-time application for integrating with the existing MIS hardware.

Dense 3D Depth Recovery for Soft Tissue Deformation During Robotically Assisted Laparoscopic Surgery

Recovering tissue deformation during robotic assisted minimally invasive surgery is an important step towards motion compensation and stabilization. This paper presents a practical strategy for dense 3D depth recovery and temporal motion tracking for deformable surfaces. The method combines image rectification with constrained disparity registration for reliable depth estimation. The accuracy and practical value of the technique is validated with a tissue phantom with known 3D geometry and motion characteristics. It has been shown that the performance of the proposed approach compares favorably against existing methods. Example results of the technique applied to in vivo robotic assisted minimally invasive surgery data are also provided.

Ambient and Wearable Sensor Fusion for Activity Recognition in Healthcare Monitoring Systems

The use of wearable sensors for home monitoring provides an effective means of inferring a patient’s level of activity. However, wearable sensors have intrinsic ambiguities that prevent certain activities to be recognized accurately. The purpose of this paper is to introduce a robust framework for enhanced activity recognition by integrating an ear-worn activity recognition (e-AR) sensor with ambient blob-based vision sensors. Accelerometer information from the e-AR is fused with features extracted from the vision sensor by using a Gaussian Mixture Model Bayes classifier. The experimental results showed a significant improvement of the classification accuracy compared to the use of the e-AR sensor alone.

Comparative Validation of Single-Shot Optical Techniques for Laparoscopic 3-D Surface Reconstruction

Intra-operative imaging techniques for obtaining the shape and morphology of soft-tissue surfaces in vivo are a key enabling technology for advanced surgical systems. Different optical techniques for 3-D surface reconstruction in laparoscopy have been proposed, however, so far no quantitative and comparative validation has been performed. Furthermore, robustness of the methods to clinically important factors like smoke or bleeding has not yet been assessed. To address these issues, we have formed a joint international initiative with the aim of validating different state-of-the-art passive and active reconstruction methods in a comparative manner. In this comprehensive in vitro study, we investigated reconstruction accuracy using different organs with various shape and texture and also tested reconstruction robustness with respect to a number of factors like the pose of the endoscope as well as the amount of blood or smoke present in the scene. The study suggests complementary advantages of the different techniques with respect to accuracy, robustness, point density, hardware complexity and computation time. While reconstruction accuracy under ideal conditions was generally high, robustness is a remaining issue to be addressed. Future work should include sensor fusion and in vivo validation studies in a specific clinical context. To trigger further research in surface reconstruction, stereoscopic data of the study will be made publically available at www.open-CAS.com upon publication of the paper.

Toward Detection and Localization of Instruments in Minimally Invasive Surgery

Methods for detecting and localizing surgical instruments in laparoscopic images are an important element of advanced robotic and computer-assisted interventions. Robotic joint encoders and sensors integrated or mounted on the instrument can provide information about the tools position, but this often has inaccuracy when transferred to the surgeons point of view. Vision sensors are currently a promising approach for determining the position of instruments in the coordinate frame of the surgical camera. In this study, we propose a vision algorithm for localizing the instruments pose in 3-D leaving only rotation in the axis of the tools shaft as an ambiguity. We propose a probabilistic supervised classification method to detect pixels in laparoscopic images that belong to surgical tools. We then use the classifier output to initialize an energy minimization algorithm for estimating the pose of a prior 3-D model of the instrument within a level set framework. We show that the proposed method is robust against noise using simulated data and we perform quantitative validation of the algorithm compared to ground truth obtained using an optical tracker. Finally, we demonstrate the practical application of the technique on in vivo data from minimally invasive surgery with traditional laparoscopic and robotic instruments.