Advanced Endoscopic Navigation:Surgical Big Data,Methodology,and Applications

随着科学技术的飞速发展，健康与环境问题日益成为人类面临的最重大问题之一。信息科学、计算机技术、电子工程与生物医学工程等学科的综合应用交叉前沿课题，研究现代工程技术方法，探索肿瘤癌症等疾病早期诊断、治疗和康复手段。本论文综述了计算机辅助微创外科手术导航、多模态医疗大数据、方法论及其临床应用：从引入微创外科手术导航概念出发，介绍了医疗大数据的术前与术中多模态医学成像方法、阐述了先进微创外科手术导航的核心流程包括计算解剖模型、术中实时导航方案、三维可视化方法及交互式软件技术，归纳了各类微创外科手术方法的临床应用。同时，重点讨论了全球各种手术导航技术在临床应用中的优缺点，分析了目前手术导航领域内的最新技术方法。在此基础上，提出了微创外科手术方法正向数字化、个性化、精准化、诊疗一体化、机器人化以及高度智能化的发展趋势。【Abstract】Interventional endoscopy (e.g., bronchoscopy, colonoscopy, laparoscopy, cystoscopy) is a widely performed procedure that involves either diagnosis of suspicious lesions or guidance for minimally invasive surgery in a variety of organs within the body cavity. Endoscopy may also be used to guide the introduction of certain items (e.g., stents) into the body. Endoscopic navigation systems seek to integrate big data with multimodal information (e.g., computed tomography, magnetic resonance images, endoscopic video sequences, ultrasound images, external trackers) relative to the patient's anatomy, control the movement of medical endoscopes and surgical tools, and guide the surgeon's actions during endoscopic interventions. Nevertheless, it remains challenging to realize the next generation of context-aware navigated endoscopy. This review presents a broad survey of various aspects of endoscopic navigation, particularly with respect to the development of endoscopic navigation techniques. First, we investigate big data with multimodal information involved in endoscopic navigation. Next, we focus on numerous methodologies used for endoscopic navigation. We then review different endoscopic procedures in clinical applications. Finally, we discuss novel techniques and promising directions for the development of endoscopic navigation.X.L. acknowledges funding from the Fundamental Research Funds for the Central Universities. T.M.P. acknowledges funding from the Canadian Foundation for Innovation, the Canadian Institutes for Health Research, the National Sciences and Engineering Research Council of Canada, and a grant from Intuitive Surgical Inc

Endoscopic video defogging using luminance blending.

Endoscopic video sequences provide surgeons with direct surgical field or visualisation on anatomical targets in the patient during robotic surgery. Unfortunately, these video images are unavoidably hazy or foggy to prevent surgeons from clear surgical vision due to typical surgical operations such as ablation and cauterisation during surgery. This Letter aims at removing fog or smoke on endoscopic video sequences to enhance and maintain a direct and clear visualisation of the operating field during robotic surgery. The authors propose a new luminance blending framework that integrates contrast enhancement with visibility restoration for foggy endoscopic video processing. The proposed method was validated on clinical endoscopic videos that were collected from robotic surgery. The experimental results demonstrate that their method provides a promising means to effectively remove fog or smoke on endoscopic video images. In particular, the visual quality of defogged endoscopic images was improved from 0.5088 to 0.6475

Endoscopic Vision Augmentation Using Multiscale Bilateral-Weighted Retinex for Robotic Surgery

医疗机器人手术视觉是微创外科手术成功与否的关键所在。由于手术器械医学电子内镜自身内在的局限性，导致了手术视野不清晰、光照不均、多烟雾等诸多问题，使得外科医生无法准确快速感知与识别人体内部器官中的神经血管以及病灶位置等结构信息，这无疑增加了手术风险和手术时间。针对这些手术视觉问题，本论文提出了一种基于双边滤波权重分析的多尺度Retinex模型方法，对达芬奇医疗机器人手术过程中所采集到的病患视频进行处理与分析。经过外科医生对实验结果的主观评价，一致认为该方法能够大幅度地增强手术视野质量；同时客观评价实验结果表明本论文所提出方法优于目前计算机视觉领域内的图像增强与恢复方法。 厦门大学信息科学与技术学院计算机科学系罗雄彪教授为本文第一作者。【Abstract】Endoscopic vision plays a significant role in minimally invasive surgical procedures. The visibility and maintenance of such direct in-situ vision is paramount not only for safety by preventing inadvertent injury, but also to improve precision and reduce operating time. Unfortunately, endoscopic vision is unavoidably degraded due to illumination variations during surgery. This work aims to restore or augment such degraded visualization and quantitatively evaluate it during robotic surgery. A multiscale bilateral-weighted retinex method is proposed to remove non-uniform and highly directional illumination and enhance surgical vision, while an objective noreference image visibility assessment method is defined in terms of sharpness, naturalness, and contrast, to quantitatively and objectively evaluate endoscopic visualization on surgical video sequences. The methods were validated on surgical data, with the experimental results showing that our method outperforms existent retinex approaches. In particular, the combined visibility was improved from 0.81 to 1.06, while three surgeons generally agreed that the results were restored with much better visibility.The authors thank the assistance of Dr. Stephen Pautler for facilitating the data acquisition, Dr. A. Jonathan McLeod and Dr.Uditha Jayarathne for helpful discussions

Biogenesis aberration: One of the mechanisms of thrombocytopenia in COVID-19

Background: The pathogenesis of COVID-19, including thrombocytopenia, has not been fully clarified. The lungs are a major organ of platelet production and thrombocytopenia induced by severe COVID-19 was proposed.Methods: the change of platelet level was analysed with clinical parameters in 95 hospitalized COVID-19 patients in Wuhan Third Hospital. The production of platelets in the lungs was explored in an ARDS rat model.Results: The level of platelets was negatively correlated with disease severity and was recovered with disease improvement. The non-survivors were accompanied by lower levels of platelet. The odds ratio (OR) of the valley level of the platelet count (PLTlow) was greater than 1, suggesting that PLTlow could be a death exposure factor. The platelet/lymphocyte ratio (PLR) was positively associated with severity of COVID-19, and the platelet/lymphocyte ratio threshold of 248.5 was best correlated with death risk (sensitivity 0.641 and specificity 0.815). To demonstrate the possible biogenesis aberration of platelet in lungs, an LPS-induced ARDS rat model was applied. Lower level of platelet in peripheral and less production of platelet from lungs in ARDS were demonstrated. Though megakaryocyte (MK) number in ARDS lungs is higher than controls, the immature platelet fraction (IPF) in postpulmonary blood is still at the same level as prepulmonary in ARDS rat, indicating that ARDS rats generated fewer platelets in lungs.Conclusion: Our data suggested that COVID-19-induced severe lung inflammation may impair platelet production in the lung. Thrombocytopenia may be mainly caused by platelet consumption for multiorgan thrombosis; however, biogenesis aberration of platelet in the lung induced by diffuse interstitial pulmonary damage cannot be ruled out

Bio-imaging and visualization for patient-customized simulations

This book contains the full papers presented at the MICCAI 2013 workshop Bio-Imaging and Visualization for Patient-Customized Simulations (MWBIVPCS 2013). MWBIVPCS 2013 brought together researchers representing several fields, such as Biomechanics, Engineering, Medicine, Mathematics, Physics and Statistic. The contributions included in this book present and discuss new trends in those fields, using several methods and techniques, including the finite element method, similarity metrics, optimization processes, graphs, hidden Markov models, sensor calibration, fuzzy logic, data mining, cellular automation, active shape models, template matching and level sets. These serve as tools to address more efficiently different and timely applications involving signal and image acquisition, image processing and analysis, image segmentation, image registration and fusion, computer simulation, image based modelling, simulation and surgical planning, image guided robot assisted surgical and image based diagnosis.  This book will appeal to researchers, PhD students, and graduate students with multidisciplinary interests related to the areas of medical imaging, image processing and analysis, computer vision, image segmentation, image registration and fusion, scientific data visualization, and image based modeling and simulation

Advanced Endoscopic Navigation: Surgical Big Data, Methodology, and Applications

© 2018 by Annual Reviews. All rights reserved. Interventional endoscopy (e.g., bronchoscopy, colonoscopy, laparoscopy, cystoscopy) is a widely performed procedure that involves either diagnosis of suspicious lesions or guidance for minimally invasive surgery in a variety of organs within the body cavity. Endoscopy may also be used to guide the introduction of certain items (e.g., stents) into the body. Endoscopic navigation systems seek to integrate big data with multimodal information (e.g., computed tomography, magnetic resonance images, endoscopic video sequences, ultrasound images, external trackers) relative to the patient\u27s anatomy, control the movement of medical endoscopes and surgical tools, and guide the surgeon\u27s actions during endoscopic interventions. Nevertheless, it remains challenging to realize the next generation of context-aware navigated endoscopy. This review presents a broad survey of various aspects of endoscopic navigation, particularly with respect to the development of endoscopic navigation techniques. First, we investigate big data with multimodal information involved in endoscopic navigation. Next, we focus on numerous methodologies used for endoscopic navigation. We then review different endoscopic procedures in clinical applications. Finally, we discuss novel techniques and promising directions for the development of endoscopic navigation

Multiscale Retinex Aggregation to Enable Robust Dense Stereo Correspondence

© 2015 IEEE. Stereo correspondence is a traditional but still challenging problem in various computer vision tasks. Although current stereo matching algorithms work well, they are still limited by occlusions, texture less and blurred structures, and particularly illumination differences. By revisiting the cost construction and aggregation step in the stereo correspondence procedure, this paper studies a multiscale retinex aggregation method to achieve accurate dense stereo matching. Our method employs the retinex theory to effectively enhance local contrast and utilize color information to boost the matching cost construction and aggregation. We evaluate our proposed framework on benchmark and surgical stereo data. The experimental results demonstrate that our multiscale retinex aggregation provides a more or comparable accurate dense stereo matching strategy. In particular, our method is robust to heavy illumination differences while giving similar performance to state-of-the-art methods on images with uniform illumination