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

Vision-based retargeting for endoscopic navigation

Endoscopy is a standard procedure for visualising the human gastrointestinal tract. With the advances in biophotonics, imaging techniques such as narrow band imaging, confocal laser endomicroscopy, and optical coherence tomography can be combined with normal endoscopy for assisting the early diagnosis of diseases, such as cancer. In the past decade, optical biopsy has emerged to be an effective tool for tissue analysis, allowing in vivo and in situ assessment of pathological sites with real-time feature-enhanced microscopic images. However, the non-invasive nature of optical biopsy leads to an intra-examination retargeting problem, which is associated with the difficulty of re-localising a biopsied site consistently throughout the whole examination. In addition to intra-examination retargeting, retargeting of a pathological site is even more challenging across examinations, due to tissue deformation and changing tissue morphologies and appearances. The purpose of this thesis is to address both the intra- and inter-examination retargeting problems associated with optical biopsy. We propose a novel vision-based framework for intra-examination retargeting. The proposed framework is based on combining visual tracking and detection with online learning of the appearance of the biopsied site. Furthermore, a novel cascaded detection approach based on random forests and structured support vector machines is developed to achieve efficient retargeting. To cater for reliable inter-examination retargeting, the solution provided in this thesis is achieved by solving an image retrieval problem, for which an online scene association approach is proposed to summarise an endoscopic video collected in the first examination into distinctive scenes. A hashing-based approach is then used to learn the intrinsic representations of these scenes, such that retargeting can be achieved in subsequent examinations by retrieving the relevant images using the learnt representations. For performance evaluation of the proposed frameworks, extensive phantom, ex vivo and in vivo experiments have been conducted, with results demonstrating the robustness and potential clinical values of the methods proposed.Open Acces

Real-time Geometry-Aware Augmented Reality in Minimally Invasive Surgery

The potential of Augmented Reality (AR) technology to assist minimally invasive surgeries (MIS) lies in its computational performanceand accuracy in dealing with challenging MIS scenes. Even with the latest hardware and software technologies, achieving both real-timeand accurate augmented information overlay in MIS is still a formidable task. In this paper, we present a novel real-time AR frameworkfor MIS that achieves interactive geometric aware augmented reality in endoscopic surgery with stereo views. Our framework tracks themovement of the endoscopic camera and simultaneously reconstructs a dense geometric mesh of the MIS scene. The movement of the camerais predicted by minimising the re-projection error to achieve a fast tracking performance, while the 3D mesh is incrementally built by a densezero mean normalised cross correlation stereo matching method to improve the accuracy of the surface reconstruction. Our proposed systemdoes not require any prior template or pre-operative scan and can infer the geometric information intra-operatively in real-time. With thegeometric information available, our proposed AR framework is able to interactively add annotations, localisation of tumors and vessels,and measurement labeling with greater precision and accuracy compared with the state of the art approaches

Computerized Evaluatution of Microsurgery Skills Training

The style of imparting medical training has evolved, over the years. The traditional methods of teaching and practicing basic surgical skills under apprenticeship model, no longer occupy the first place in modern technically demanding advanced surgical disciplines like neurosurgery. Furthermore, the legal and ethical concerns for patient safety as well as cost-effectiveness have forced neurosurgeons to master the necessary microsurgical techniques to accomplish desired results. This has lead to increased emphasis on assessment of clinical and surgical techniques of the neurosurgeons. However, the subjective assessment of microsurgical techniques like micro-suturing under the apprenticeship model cannot be completely unbiased. A few initiatives using computer-based techniques, have been made to introduce objective evaluation of surgical skills. This thesis presents a novel approach involving computerized evaluation of different components of micro-suturing techniques, to eliminate the bias of subjective assessment. The work involved acquisition of cine clips of micro-suturing activity on synthetic material. Image processing and computer vision based techniques were then applied to these videos to assess different characteristics of micro-suturing viz. speed, dexterity and effectualness. In parallel subjective grading on these was done by a senior neurosurgeon. Further correlation and comparative study of both the assessments was done to analyze the efficacy of objective and subjective evaluation

Retrieval and Registration of Long-Range Overlapping Frames for Scalable Mosaicking of In Vivo Fetoscopy

Purpose: The standard clinical treatment of Twin-to-Twin Transfusion Syndrome consists in the photo-coagulation of undesired anastomoses located on the placenta which are responsible to a blood transfer between the two twins. While being the standard of care procedure, fetoscopy suffers from a limited field-of-view of the placenta resulting in missed anastomoses. To facilitate the task of the clinician, building a global map of the placenta providing a larger overview of the vascular network is highly desired. Methods: To overcome the challenging visual conditions inherent to in vivo sequences (low contrast, obstructions or presence of artifacts, among others), we propose the following contributions: (i) robust pairwise registration is achieved by aligning the orientation of the image gradients, and (ii) difficulties regarding long-range consistency (e.g. due to the presence of outliers) is tackled via a bag-of-word strategy, which identifies overlapping frames of the sequence to be registered regardless of their respective location in time. Results: In addition to visual difficulties, in vivo sequences are characterised by the intrinsic absence of gold standard. We present mosaics motivating qualitatively our methodological choices and demonstrating their promising aspect. We also demonstrate semi-quantitatively, via visual inspection of registration results, the efficacy of our registration approach in comparison to two standard baselines. Conclusion: This paper proposes the first approach for the construction of mosaics of placenta in in vivo fetoscopy sequences. Robustness to visual challenges during registration and long-range temporal consistency are proposed, offering first positive results on in vivo data for which standard mosaicking techniques are not applicable.Comment: Accepted for publication in International Journal of Computer Assisted Radiology and Surgery (IJCARS

Vision-based and marker-less surgical tool detection and tracking: a review of the literature

In recent years, tremendous progress has been made in surgical practice for example with Minimally Invasive Surgery (MIS). To overcome challenges coming from deported eye-to-hand manipulation, robotic and computer-assisted systems have been developed. Having real-time knowledge of the pose of surgical tools with respect to the surgical camera and underlying anatomy is a key ingredient for such systems. In this paper, we present a review of the literature dealing with vision-based and marker-less surgical tool detection. This paper includes three primary contributions: (1) identification and analysis of data-sets used for developing and testing detection algorithms, (2) in-depth comparison of surgical tool detection methods from the feature extraction process to the model learning strategy and highlight existing shortcomings, and (3) analysis of validation techniques employed to obtain detection performance results and establish comparison between surgical tool detectors. The papers included in the review were selected through PubMed and Google Scholar searches using the keywords: “surgical tool detection”, “surgical tool tracking”, “surgical instrument detection” and “surgical instrument tracking” limiting results to the year range 2000 2015. Our study shows that despite significant progress over the years, the lack of established surgical tool data-sets, and reference format for performance assessment and method ranking is preventing faster improvement