An Augmented Reality Platform for Preoperative Surgical Planning

Researching in new technologies for diagnosis, planning and medical treatment have allowed the development of computer tools that provide new ways of representing data obtained from patient's medical images such as computed tomography (CT) and magnetic resonance imaging (MRI). In this sense, augmented reality (AR) technologies provide a new form of data representation by combining the common analysis using images and the ability to superimpose virtual 3D representations of the organs of the human body in the real environment. In this paper the development of a generic computer platform based on augmented reality technology for surgical preoperative planning is presented. In particular, the surgeon can navigate in the 3D models of the patient's organs in order to have the possibility to perfectly understand the anatomy and plan in the best way the surgical procedure. In addition, a touchless interaction with the virtual organs is available thanks to the use of an armband provided of electromiographic muscle sensors. To validate the system, we focused in a navigation through aorta artery for mitral valve repair surgery

The Challenge of Augmented Reality in Surgery

Imaging has revolutionized surgery over the last 50 years. Diagnostic imaging is a key tool for deciding to perform surgery during disease management; intraoperative imaging is one of the primary drivers for minimally invasive surgery (MIS), and postoperative imaging enables effective follow-up and patient monitoring. However, notably, there is still relatively little interchange of information or imaging modality fusion between these different clinical pathway stages. This book chapter provides a critique of existing augmented reality (AR) methods or application studies described in the literature using relevant examples. The aim is not to provide a comprehensive review, but rather to give an indication of the clinical areas in which AR has been proposed, to begin to explain the lack of clinical systems and to provide some clear guidelines to those intending pursue research in this area

Artificial Intelligence for Emerging Technology in Surgery: Systematic Review and Validation

Surgery is a high-risk procedure of therapy and is associated to post trauma complications of longer hospital stay, estimated blood loss and long duration of surgeries. Reports have suggested that over 2.5% patients die during and post operation. This paper is aimed at systematic review of previous research on artificial intelligence (AI) in surgery, analyzing their results with suitable software to validate their research by obtaining same or contrary results. Six published research articles have been reviewed across three continents. These articles have been re-validated using software including SPSS and MedCalc to obtain the statistical features such as the mean, standard deviation, significant level, and standard error. From the significant values, the experiments are then classified according to the null (p0.05) hypotheses. The results obtained from the analysis have suggested significant difference in operating time, docking time, staging time, and estimated blood loss but show no significant difference in length of hospital stay, recovery time and lymph nodes harvested between robotic assisted surgery using AI and normal conventional surgery. From the evaluations, this research suggests that AI-assisted surgery improves over the conventional surgery as safer and more efficient system of surgery with minimal or no complications

Haptic assessment of tissue stiffness in locating and identifying gynaecological cancer in human tissue

Gynaecological surgeons are not able to gather adequate tissue feedback during minimal access surgery for cancer treatment. This can result in failure to locate tumour boundaries and to ensure these are completely resected within tumour-free resection margins. Surgeons achieve significantly better surgical and oncological outcomes if they can identify the precise location of a gynaecological tumour. Indeed, the true nature of tumour, whether benign or cancerous, is often not known prior to surgery. If more details were available in relation to the characteristics that differentiate gynaecological cancer in tumours, this would enable more accurate diagnosis and help in the planning of surgery. HYPOTHESIS: Haptic technology has the potential to enhance the surgeon’s degree of perception during minimal access surgery. Alteration in tissue stiffness in gynaecological tumours, thought to be associated with the accelerated multiplication of cancer cells, should allow their location to be identified and help in determining the likelihood of malignancy. METHOD: Setting: (i) Guy's & St Thomas' Hospital (ii) Dept of Informatics (King's College London).Permission from the National Research Ethics Committee and Research & Development (R&D) approval were sought from the National Health Service. The Phantom Omni, capable of 3D motion tracking, attached to a nano-17 force sensor, was used to capture real-time position data and force data. Uniaxial indentation palpation behaviour was used. The indentation depth was calculated using the displacement of the probe from the surface to the deepest point for each contact. The tissue stiffness (TS) was then calculated.The haptic probe was tested first on silicone models with embedded nodules mimicking tumour(s). This was followed by assessing TS ex-vivo using a haptic probe on fresh human gynaecological organs that had been removed in surgery. Tissue stiffness maps were generated in real time using the haptic device by converting stiffness values into RGB values. Surgeons also manually palpated and recorded the site of the tumour. Histology was used as the gold standard for location and cancer diagnosis. Manual palpation and haptic data were compared for accuracy on tumour location. The tissue stiffness calculated by the haptic probe was compared in cancer and control specimens. Several data analysis techniques were applied to derive results.CONTRIBUTIONS: Haptic indentation probe was tested for the first time on fresh human gynaecological organs to locate cancer in a clinical setting. We are the first one to evaluate the accuracy of cancer diagnosis in human gynaecological organs with a force sensing haptic indentation probe measuring tissue stiffness

A deep learning framework for real-time 3D model registration in robot-assisted laparoscopic surgery

Introduction The current study presents a deep learning framework to determine, in real-time, position and rotation of a target organ from an endoscopic video. These inferred data are used to overlay the 3D model of patient's organ over its real counterpart. The resulting augmented video flow is streamed back to the surgeon as a support during laparoscopic robot-assisted procedures. Methods This framework exploits semantic segmentation and, thereafter, two techniques, based on Convolutional Neural Networks and motion analysis, were used to infer the rotation. Results The segmentation shows optimal accuracies, with a mean IoU score greater than 80% in all tests. Different performance levels are obtained for rotation, depending on the surgical procedure. Discussion Even if the presented methodology has various degrees of precision depending on the testing scenario, this work sets the first step for the adoption of deep learning and augmented reality to generalise the automatic registration process

Using Contours as Boundary Conditions for Elastic Registration during Minimally Invasive Hepatic Surgery

International audienceWe address in this paper the ill-posed problem of initial alignment of pre-operative to intra-operative data for augmented reality during minimally invasive hepatic surgery. This problem consists of finding the rigid transformation that relates the scanning reference and the endoscopic camera pose, and the non-rigid transformation undergone by the liver w.r.t its scanned state. Most of the state-of-the-art methods assume a known initial registration. Here, we propose a method that permits to recover the deformation undergone by the liver while simultaneously finding the rotational and translational parts of the transformation. Our formulation considers the boundaries of the liver with its surrounding tissues as hard constraints directly encoded in an energy minimization process. We performed experiments on real in-vivo data of human hepatic surgery and synthetic data, and compared our method with related works

Deformable Registration of a Preoperative 3D Liver Volume to a Laparoscopy Image Using Contour and Shading Cues

The deformable registration of a preoperative organ volume to an intraoperative laparoscopy image is required to achieve augmented reality in laparoscopy. This is an extremely challenging objective for the liver. This is because the preoperative volume is textureless, and the liver is deformed and only partially visible in the laparoscopy image. We solve this problem by modeling the preoperative volume as a Neo-Hookean elastic model, which we evolve under shading and contour cues. The contour cues combine the organ’s silhouette and a few curvilinear anatomical landmarks. The problem is difficult because the shading cue is highly nonconvex and the contour cues give curve-level (and not point-level) correspondences. We propose a convergent alternating projections algorithm, which achieves a $$4$$ registration error

Silhouette-based Pose Estimation for Deformable Organs Application to Surgical Augmented Reality

International audience— In this paper we introduce a method for semi-automatic registration of 3D deformable models using 2D shape outlines (silhouettes) extracted from a monocular camera view. Our framework is based on the combination of a biomechanical model of the organ with a set of projective constraints influencing the deformation of the model. To enforce convergence towards a global minimum for this ill-posed problem we interactively provide a rough (rigid) estimation of the pose. We show that our approach allows for the estimation of the non-rigid 3D pose while relying only on 2D information. The method is evaluated experimentally on a soft silicone gel model of a liver, as well as on real surgical data, providing augmented reality of the liver and the kidney using a monocular laparoscopic camera. Results show that the final elastic registration can be obtained in just a few seconds, thus remaining compatible with clinical constraints. We also evaluate the sensitivity of our approach according to both the initial alignment of the model and the silhouette length and shape

Endometriosis

This book provides an insight into the emerging trends in pathogenesis, diagnosis and management of endometriosis. Key features of the book include overviews of endometriosis; endometrial angiogenesis, stem cells involvement, immunological and hormonal aspects related to the disease pathogenesis; recent research reports on infertility, endometrial receptivity, ovarian cancer and altered gene expression associated with endometriosis; various predictive markers, and imaging modalities including MRI and ultrasound for efficient diagnosis; as well as current non-hormonal and hormonal treatment strategies This book is expected to be a valuable resource for clinicians, scientists and students who would like to have an improved understanding of endometriosis and also appreciate recent research trends associated with this disease