Comprehensive review of fluorescence applications in gynecology

Since the introduction of indocyanine green (ICG) as a fluorophore in near-infrared imaging, fluorescence visualization has become an essential tool in many fields of surgery. In the field of gynecology, recent new applications have been proposed and found their place in clinical practice. Different applications in gynecology were investigated, subcategorized, and overviewed concerning surgical applications and available dyes. Specific applications in which fluorescence-guided surgery was implemented in gynecology are described in this manuscript—namely, sentinel node biopsy, mesometrium visualization, angiography of different organs, safety issues in pregnant women, ureters visualization, detection of peritoneal metastases, targeted fluorophores for cancer detection, fluorescent contamination hysterectomy, lymphography for lower limb lymphedema prevention, tumor margin detection, endometriosis, and metastases mapping. With evolving technology, further innovative research on the new applications of fluorescence visualization in cancer surgery may help to establish these techniques as standards of high-quality surgery in gynecology. However, more investigations are necessary in order to assess if these innovative tools can also be effective to improve patient outcomes and quality of life in different gynecologic malignancies

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

Multispectral-Fluorescence Imaging as a Tool to Separate Healthy from Disease-Related Lymphatic Anatomy During Robot-Assisted Laparoscopy

Prostatic carcinom

Initial experience of ureteric visualization using methylene blue during laparoscopy for gynecological surgery

Objectives: Iatrogenic ureteral injury is a severe surgical complication, with a highest incidence of 1.5% in gynecological surgeries. The purpose of this report is to document our initial experience with using methylene blue (MB) to label the ureter in gynecological laparoscopic surgeries and to explore its effectiveness and safety. This is also a novel description of simultaneously visualizing ureteral MB fluorescence and sentinel lymph nodes (SLN's) Indocyanine Green (ICG) fluorescence using the same camera. Methods: This study included patients undergoing gynecological laparoscopic surgeries, with the same surgeon performing all cases. During the early stages of each surgery, rapid intravenous infusion of MB was administered. For cases requiring SLN imaging, we also injected ICG solution into the cervix. Assessment of the included cases was conducted both intraoperatively and postoperatively. The group that had MB fluorescence (Group A) was compared to a control group that did not have it (Group B). Results: A total of 25 patients (Group A) received MB during surgery, demonstrating 45 ureters clearly, with an imaging success rate of 90%. Continuous and clearer fluorescence imaging was achieved in cases with ureteral hydronephrosis. In most patients, ureteral fluorescence was visible 15–20 min after intravenous infusion of MB, and 64% still exhibited fluorescence at the end of the surgery. In patients who had both ICG and MB, dual fluorescence imaging was achieved clearly. Among the included cases, there were no iatrogenic ureteral injuries (0%), which we observed to be lower than in patients who did not receive MB (1.3%). The rate of adverse events was similar in both groups. Conclusion: Using MB fluorescence is an effective and safe method of visualizing the ureters during gynecological surgeries, and can diminish iatrogenic ureteral injury without increased associated adverse events. It therefore may offer promising prospects for clinical application

Multispectral image analysis in laparoscopy – A machine learning approach to live perfusion monitoring

Modern visceral surgery is often performed through small incisions. Compared to open surgery, these minimally invasive interventions result in smaller scars, fewer complications and a quicker recovery. While to the patients benefit, it has the drawback of limiting the physician’s perception largely to that of visual feedback through a camera mounted on a rod lens: the laparoscope. Conventional laparoscopes are limited by “imitating” the human eye. Multispectral cameras remove this arbitrary restriction of recording only red, green and blue colors. Instead, they capture many specific bands of light. Although these could help characterize important indications such as ischemia and early stage adenoma, the lack of powerful digital image processing prevents realizing the technique’s full potential. The primary objective of this thesis was to pioneer fluent functional multispectral imaging (MSI) in laparoscopy. The main technical obstacles were: (1) The lack of image analysis concepts that provide both high accuracy and speed. (2) Multispectral image recording is slow, typically ranging from seconds to minutes. (3) Obtaining a quantitative ground truth for the measurements is hard or even impossible. To overcome these hurdles and enable functional laparoscopy, for the first time in this field physical models are combined with powerful machine learning techniques. The physical model is employed to create highly accurate simulations, which in turn teach the algorithm to rapidly relate multispectral pixels to underlying functional changes. To reduce the domain shift introduced by learning from simulations, a novel transfer learning approach automatically adapts generic simulations to match almost arbitrary recordings of visceral tissue. In combination with the only available video-rate capable multispectral sensor, the method pioneers fluent perfusion monitoring with MSI. This system was carefully tested in a multistage process, involving in silico quantitative evaluations, tissue phantoms and a porcine study. Clinical applicability was ensured through in-patient recordings in the context of partial nephrectomy; in these, the novel system characterized ischemia live during the intervention. Verified against a fluorescence reference, the results indicate that fluent, non-invasive ischemia detection and monitoring is now possible. In conclusion, this thesis presents the first multispectral laparoscope capable of videorate functional analysis. The system was successfully evaluated in in-patient trials, and future work should be directed towards evaluation of the system in a larger study. Due to the broad applicability and the large potential clinical benefit of the presented functional estimation approach, I am confident the descendants of this system are an integral part of the next generation OR

Translational Functional Imaging in Surgery Enabled by Deep Learning

Many clinical applications currently rely on several imaging modalities such as Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), Computed Tomography (CT), etc. All such modalities provide valuable patient data to the clinical staff to aid clinical decision-making and patient care. Despite the undeniable success of such modalities, most of them are limited to preoperative scans and focus on morphology analysis, e.g. tumor segmentation, radiation treatment planning, anomaly detection, etc. Even though the assessment of different functional properties such as perfusion is crucial in many surgical procedures, it remains highly challenging via simple visual inspection. Functional imaging techniques such as Spectral Imaging (SI) link the unique optical properties of different tissue types with metabolism changes, blood flow, chemical composition, etc. As such, SI is capable of providing much richer information that can improve patient treatment and care. In particular, perfusion assessment with functional imaging has become more relevant due to its involvement in the treatment and development of several diseases such as cardiovascular diseases. Current clinical practice relies on Indocyanine Green (ICG) injection to assess perfusion. Unfortunately, this method can only be used once per surgery and has been shown to trigger deadly complications in some patients (e.g. anaphylactic shock). This thesis addressed common roadblocks in the path to translating optical functional imaging modalities to clinical practice. The main challenges that were tackled are related to a) the slow recording and processing speed that SI devices suffer from, b) the errors introduced in functional parameter estimations under changing illumination conditions, c) the lack of medical data, and d) the high tissue inter-patient heterogeneity that is commonly overlooked. This framework follows a natural path to translation that starts with hardware optimization. To overcome the limitation that the lack of labeled clinical data and current slow SI devices impose, a domain- and task-specific band selection component was introduced. The implementation of such component resulted in a reduction of the amount of data needed to monitor perfusion. Moreover, this method leverages large amounts of synthetic data, which paired with unlabeled in vivo data is capable of generating highly accurate simulations of a wide range of domains. This approach was validated in vivo in a head and neck rat model, and showed higher oxygenation contrast between normal and cancerous tissue, in comparison to a baseline using all available bands. The need for translation to open surgical procedures was met by the implementation of an automatic light source estimation component. This method extracts specular reflections from low exposure spectral images, and processes them to obtain an estimate of the light source spectrum that generated such reflections. The benefits of light source estimation were demonstrated in silico, in ex vivo pig liver, and in vivo human lips, where the oxygenation estimation error was reduced when utilizing the correct light source estimated with this method. These experiments also showed that the performance of the approach proposed in this thesis surpass the performance of other baseline approaches. Video-rate functional property estimation was achieved by two main components: a regression and an Out-of-Distribution (OoD) component. At the core of both components is a compact SI camera that is paired with state-of-the-art deep learning models to achieve real time functional estimations. The first of such components features a deep learning model based on a Convolutional Neural Network (CNN) architecture that was trained on highly accurate physics-based simulations of light-tissue interactions. By doing this, the challenge of lack of in vivo labeled data was overcome. This approach was validated in the task of perfusion monitoring in pig brain and in a clinical study involving human skin. It was shown that this approach is capable of monitoring subtle perfusion changes in human skin in an arm clamping experiment. Even more, this approach was capable of monitoring Spreading Depolarizations (SDs) (deoxygenation waves) in the surface of a pig brain. Even though this method is well suited for perfusion monitoring in domains that are well represented with the physics-based simulations on which it was trained, its performance cannot be guaranteed for outlier domains. To handle outlier domains, the task of ischemia monitoring was rephrased as an OoD detection task. This new functional estimation component comprises an ensemble of Invertible Neural Networks (INNs) that only requires perfused tissue data from individual patients to detect ischemic tissue as outliers. The first ever clinical study involving a video-rate capable SI camera in laparoscopic partial nephrectomy was designed to validate this approach. Such study revealed particularly high inter-patient tissue heterogeneity under the presence of pathologies (cancer). Moreover, it demonstrated that this personalized approach is now capable of monitoring ischemia at video-rate with SI during laparoscopic surgery. In conclusion, this thesis addressed challenges related to slow image recording and processing during surgery. It also proposed a method for light source estimation to facilitate translation to open surgical procedures. Moreover, the methodology proposed in this thesis was validated in a wide range of domains: in silico, rat head and neck, pig liver and brain, and human skin and kidney. In particular, the first clinical trial with spectral imaging in minimally invasive surgery demonstrated that video-rate ischemia monitoring is now possible with deep learning

Image-guided cancer surgery : the value of near-infrared fluorescence imaging during oncologic and gastrointestinal procedures

Intraoperative imaging using near-infrared (NIR) fluorescence is a relatively new technique that can be used to visualize tumor tissue, sentinel nodes and vital anatomical structures. This thesis is divided in three parts. In part one the ability to visualize surgical margins using NIR fluorescence imaging is demonstrated. Tumor visualization is established using the clinically approved contrast agent indocyanine green, as well as newly developed tumor targeted probes. The proportion of laparoscopic procedures has steadily increased over the last two decades. A challenging aspect of this conversion to minimal invasive surgery is the lack of tactile information, making it of particular interest for the development and improvement of laparoscopic NIR fluorescence imaging systems. Part two focusses on the clinical implementation of NIR fluorescence guided sentinel lymph node mapping for several indications (e.g. breast, skin and vulvar cancer). Besides visualization of structures that need to be resected (e.g. tumor tissue or sentinel nodes), NIR fluorescence has also the potential to be of value for the identification of structures that should be spared. In part three, we demonstrate the first-in-human application of NIR fluorescence guided ureteral visualization, and also the optimization of bile duct imaging for routine laparoscopic cholecystectomies.Dutch Cancer Society (UL 2010-4732), the Center for Translational Molecular Medicine (CTMM, DeCoDe and MUSIS projects) and the Leiden University Fund/Piso KuperusUBL - phd migration 201