Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy

Objective: Surgical data science is evolving into a research field that aims to observe everything occurring within and around the treatment process to provide situation-aware data-driven assistance. In the context of endoscopic video analysis, the accurate classification of organs in the field of view of the camera proffers a technical challenge. Herein, we propose a new approach to anatomical structure classification and image tagging that features an intrinsic measure of confidence to estimate its own performance with high reliability and which can be applied to both RGB and multispectral imaging (MI) data. Methods: Organ recognition is performed using a superpixel classification strategy based on textural and reflectance information. Classification confidence is estimated by analyzing the dispersion of class probabilities. Assessment of the proposed technology is performed through a comprehensive in vivo study with seven pigs. Results: When applied to image tagging, mean accuracy in our experiments increased from 65% (RGB) and 80% (MI) to 90% (RGB) and 96% (MI) with the confidence measure. Conclusion: Results showed that the confidence measure had a significant influence on the classification accuracy, and MI data are better suited for anatomical structure labeling than RGB data. Significance: This work significantly enhances the state of art in automatic labeling of endoscopic videos by introducing the use of the confidence metric, and by being the first study to use MI data for in vivo laparoscopic tissue classification. The data of our experiments will be released as the first in vivo MI dataset upon publication of this paper.Comment: 7 pages, 6 images, 2 table

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-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. 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 opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions

Surgical spectral imaging

Recent technological developments have resulted in the availability of miniaturised spectral imaging sensors capable of operating in the multi- (MSI) and hyperspectral imaging (HSI) regimes. Simultaneous advances in image-processing techniques and artificial intelligence (AI), especially in machine learning and deep learning, have made these data-rich modalities highly attractive as a means of extracting biological information non-destructively. Surgery in particular is poised to benefit from this, as spectrally-resolved tissue optical properties can offer enhanced contrast as well as diagnostic and guidance information during interventions. This is particularly relevant for procedures where inherent contrast is low under standard white light visualisation. This review summarises recent work in surgical spectral imaging (SSI) techniques, taken from Pubmed, Google Scholar and arXiv searches spanning the period 2013–2019. New hardware, optimised for use in both open and minimally-invasive surgery (MIS), is described, and recent commercial activity is summarised. Computational approaches to extract spectral information from conventional colour images are reviewed, as tip-mounted cameras become more commonplace in MIS. Model-based and machine learning methods of data analysis are discussed in addition to simulation, phantom and clinical validation experiments. A wide variety of surgical pilot studies are reported but it is apparent that further work is needed to quantify the clinical value of MSI/HSI. The current trend toward data-driven analysis emphasises the importance of widely-available, standardised spectral imaging datasets, which will aid understanding of variability across organs and patients, and drive clinical translation

Design and Integration of Electrical Bio-Impedance Sensing in a Bipolar Forceps for Soft Tissue Identification: A Feasibility Study

This paper presents the integration of electrical bio-impedance sensing technology into a bipolar surgical forceps for soft tissue identification during a robotic assisted procedure. The EBI sensing is done by pressing the forceps on the target tissue with a controlled pressing depth and a controlled jaw opening distance. The impact of these 2 parameters are characterized by finite element simulation. Subsequently, an experiment is conducted with 4 types of ex-vivo tissues including liver, kidney, lung and muscle. The experimental results demonstrate that the proposed EBI sensing method can identify these 4 tissue types with an accuracy higher than 92.82%

25th International Congress of the European Association for Endoscopic Surgery (EAES) Frankfurt, Germany, 14-17 June 2017 : Oral Presentations

Introduction: Ouyang has recently proposed hiatal surface area (HSA) calculation by multiplanar multislice computer tomography (MDCT) scan as a useful tool for planning treatment of hiatus defects with hiatal hernia (HH), with or without gastroesophageal reflux (MRGE). Preoperative upper endoscopy or barium swallow cannot predict the HSA and pillars conditions. Aim to asses the efficacy of MDCT’s calculation of HSA for planning the best approach for the hiatal defects treatment. Methods: We retrospectively analyzed 25 patients, candidates to laparoscopic antireflux surgery as primary surgery or hiatus repair concomitant with or after bariatric surgery. Patients were analyzed preoperatively and after one-year follow-up by MDCT scan measurement of esophageal hiatus surface. Five normal patients were enrolled as control group. The HSA’s intraoperative calculation was performed after complete dissection of the area considered a triangle. Postoperative CT-scan was done after 12 months or any time reflux symptoms appeared. Results: (1) Mean HSA in control patients with no HH, no MRGE was cm2 and similar in non-complicated patients with previous LSG and cruroplasty. (2) Mean HSA in patients candidates to cruroplasty was 7.40 cm2. (3) Mean HSA in patients candidates to redo cruroplasty for recurrence was 10.11 cm2. Discussion. MDCT scan offer the possibility to obtain an objective measurement of the HSA and the correlation with endoscopic findings and symptoms. The preoperative information allow to discuss with patients the proper technique when a HSA[5 cm2 is detected. During the follow-up a correlation between symptoms and failure of cruroplasty can be assessed. Conclusions: MDCT scan seems to be an effective non-invasive method to plan hiatal defect treatment and to check during the follow-up the potential recurrence. Future research should correlate in larger series imaging data with intraoperative findings

A Review on Advances in Intra-operative Imaging for Surgery and Therapy: Imagining the Operating Room of the Future

none4openZaffino, Paolo; Moccia, Sara; De Momi, Elena; Spadea, Maria FrancescaZaffino, Paolo; Moccia, Sara; De Momi, Elena; Spadea, Maria Francesc

Supervised cnn strategies for optical image segmentation and classification in interventional medicine

The analysis of interventional images is a topic of high interest for the medical-image analysis community. Such an analysis may provide interventional-medicine professionals with both decision support and context awareness, with the final goal of improving patient safety. The aim of this chapter is to give an overview of some of the most recent approaches (up to 2018) in the field, with a focus on Convolutional Neural Networks (CNNs) for both segmentation and classification tasks. For each approach, summary tables are presented reporting the used dataset, involved anatomical region and achieved performance. Benefits and disadvantages of each approach are highlighted and discussed. Available datasets for algorithm training and testing and commonly used performance metrics are summarized to offer a source of information for researchers that are approaching the field of interventional-image analysis. The advancements in deep learning for medical-image analysis are involving more and more the interventional-medicine field. However, these advancements are undeniably slower than in other fields (e.g. preoperative-image analysis) and considerable work still needs to be done in order to provide clinicians with all possible support during interventional-medicine procedures

Situation Interpretation for Knowledge- and Model Based Laparoscopic Surgery

To manage the influx of information into surgical practice, new man-machine interaction methods are necessary to prevent information overflow. This work presents an approach to automatically segment surgeries into phases and select the most appropriate pieces of information for the current situation. This way, assistance systems can adopt themselves to the needs of the surgeon and not the other way around

Visual SLAM for Measurement and Augmented Reality in Laparoscopic Surgery

In spite of the great advances in laparoscopic surgery, this type of surgery still shows some difficulties during its realization, mainly caused by its complex maneuvers and, above all, by the loss of the depth perception. Unlike classical open surgery --laparotomy-- where surgeons have direct contact with organs and a complete 3D perception, laparoscopy is carried out by means of specialized instruments, and a monocular camera (laparoscope) in which the 3D scene is projected into a 2D plane --image. The main goal of this thesis is to face with this loss of depth perception by making use of Simultaneous Localization and Mapping (SLAM) algorithms developed in the fields of robotics and computer vision during the last years. These algorithms allow to localize, in real time (25 $\thicksim$ 30 frames per second), a camera that moves freely inside an unknown rigid environment while, at the same time, they build a map of this environment by exploiting images gathered by that camera. These algorithms have been extensively validated both in man-made environments (buildings, rooms, ...) and in outdoor environments, showing robustness to occlusions, sudden camera motions, or clutter. This thesis tries to extend the use of these algorithms to laparoscopic surgery. Due to the intrinsic nature of internal body images (they suffer from deformations, specularities, variable illumination conditions, limited movements, ...), applying this type of algorithms to laparoscopy supposes a real challenge. Knowing the camera (laparoscope) location with respect to the scene (abdominal cavity) and the 3D map of that scene opens new interesting possibilities inside the surgical field. This knowledge enables to do augmented reality annotations directly on the laparoscopic images (e.g. alignment of preoperative 3D CT models); intracavity 3D distance measurements; or photorealistic 3D reconstructions of the abdominal cavity recovering synthetically the lost depth. These new facilities provide security and rapidity to surgical procedures without disturbing the classical procedure workflow. Hence, these tools are available inside the surgeon's armory, being the surgeon who decides to use them or not. Additionally, knowledge of the camera location with respect to the patient's abdominal cavity is fundamental for future development of robots that can operate automatically since, knowing this location, the robot will be able to localize other tools controlled by itself with respect to the patient. In detail, the contributions of this thesis are: - To demonstrate the feasibility of applying SLAM algorithms to laparoscopy showing experimentally that using robust data association is a must. - To robustify one of these algorithms, in particular the monocular EKF-SLAM algorithm, by adapting a relocalization system and improving data association with a robust matching algorithm. - To develop of a robust matching method (1-Point RANSAC algorithm). - To develop a new surgical procedure to ease the use of visual SLAM in laparoscopy. - To make an extensive validation of the robust EKF-SLAM (EKF + relocalization + 1-Point RANSAC) obtaining millimetric errors and working in real time both on simulation and real human surgeries. The selected surgery has been the ventral hernia repair. - To demonstrate the potential of these algorithms in laparoscopy: they recover synthetically the depth of the operative field which is lost by using monocular laparoscopes, enable the insertion of augmented reality annotations, and allow to perform distance measurements using only a laparoscopic tool (to define the real scale) and laparoscopic images. - To make a clinical validation showing that these algorithms allow to shorten surgical times of operations and provide more security to the surgical procedures