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Augmented reality for laparoscopic liver resection is a visualisation mode that allows a surgeon to localise tumours and vessels embedded within the liver by projecting them on top of a laparoscopic image. Preoperative 3D models extracted from Computed Tomography (CT) or Magnetic Resonance (MR) imaging data are registered to the intraoperative laparoscopic images during this process. Regarding 3D-2D fusion, most algorithms use anatomical landmarks to guide registration, such as the liver's inferior ridge, the falciform ligament, and the occluding contours. These are usually marked by hand in both the laparoscopic image and the 3D model, which is time-consuming and prone to error. Therefore, there is a need to automate this process so that augmented reality can be used effectively in the operating room. We present the Preoperative-to-Intraoperative Laparoscopic Fusion challenge (P2ILF), held during the Medical Image Computing and Computer Assisted Intervention (MICCAI 2022) conference, which investigates the possibilities of detecting these landmarks automatically and using them in registration. The challenge was divided into two tasks: (1) A 2D and 3D landmark segmentation task and (2) a 3D-2D registration task. The teams were provided with training data consisting of 167 laparoscopic images and 9 preoperative 3D models from 9 patients, with the corresponding 2D and 3D landmark annotations. A total of 6 teams from 4 countries participated in the challenge, whose results were assessed for each task independently. All the teams proposed deep learning-based methods for the 2D and 3D landmark segmentation tasks and differentiable rendering-based methods for the registration task. The proposed methods were evaluated on 16 test images and 2 preoperative 3D models from 2 patients. In Task 1, the teams were able to segment most of the 2D landmarks, while the 3D landmarks showed to be more challenging to segment. In Task 2, only one team obtained acceptable qualitative and quantitative registration results. Based on the experimental outcomes, we propose three key hypotheses that determine current limitations and future directions for research in this domain., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: The interest in artificial intelligence (AI) is increasing. Systematic reviews suggest that there are many machine learning algorithms in surgery, however, only a minority of the studies integrate AI applications in clinical workflows. Our objective was to design and evaluate a concept to use different kinds of AI for decision support in oncological liver surgery along the treatment path., Methods: In an exploratory co-creation between design experts, surgeons, and data scientists, pain points along the treatment path were identified. Potential designs for AI-assisted solutions were developed and iteratively refined. Finally, an evaluation of the design concept was performed with n = 20 surgeons to get feedback on the different functionalities and evaluate the usability with the System Usability Scale (SUS). Participating surgeons had a mean of 14.0 ± 5.0 years of experience after graduation., Results: The design concept was named "Aliado". Five different scenarios were identified where AI could support surgeons. Mean score of SUS was 68.2 ( ± 13.6 SD). The highest valued functionalities were "individualized prediction of survival, short-term mortality and morbidity", and "individualized recommendation of surgical strategy"., Conclusion: Aliado is a design prototype that shows how AI could be integrated into the clinical workflow. Even without a fleshed out user interface, the SUS already yielded borderline good results. Expert surgeons rated the functionalities favorably, and most of them expressed their willingness to work with a similar application in the future. Thus, Aliado can serve as a surgical vision of how an ideal AI-based assistance could look like., Competing Interests: Declaration of Competing interest The authors have no conflict of interest to disclose., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Formalizing surgical activities as triplets of the used instruments, actions performed, and target anatomies is becoming a gold standard approach for surgical activity modeling. The benefit is that this formalization helps to obtain a more detailed understanding of tool-tissue interaction which can be used to develop better Artificial Intelligence assistance for image-guided surgery. Earlier efforts and the CholecTriplet challenge introduced in 2021 have put together techniques aimed at recognizing these triplets from surgical footage. Estimating also the spatial locations of the triplets would offer a more precise intraoperative context-aware decision support for computer-assisted intervention. This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection. It includes weakly-supervised bounding box localization of every visible surgical instrument (or tool), as the key actors, and the modeling of each tool-activity in the form of ‹instrument, verb, target› triplet. The paper describes a baseline method and 10 new deep learning algorithms presented at the challenge to solve the task. It also provides thorough methodological comparisons of the methods, an in-depth analysis of the obtained results across multiple metrics, visual and procedural challenges; their significance, and useful insights for future research directions and applications in surgery., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Purpose: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset. In this work we investigated the generalizability of phase recognition algorithms in a multicenter setting including more difficult recognition tasks such as surgical action and surgical skill., Methods: To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 h was created. Labels included framewise annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 international Endoscopic Vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 research teams trained and submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment., Results: F1-scores were achieved for phase recognition between 23.9% and 67.7% (n = 9 teams), for instrument presence detection between 38.5% and 63.8% (n = 8 teams), but for action recognition only between 21.8% and 23.3% (n = 5 teams). The average absolute error for skill assessment was 0.78 (n = 1 team)., Conclusion: Surgical workflow and skill analysis are promising technologies to support the surgical team, but there is still room for improvement, as shown by our comparison of machine learning algorithms. This novel HeiChole benchmark can be used for comparable evaluation and validation of future work. In future studies, it is of utmost importance to create more open, high-quality datasets in order to allow the development of artificial intelligence and cognitive robotics in surgery., Competing Interests: Declaration of Competing Interest M. Wagner, B.-P. Müller-Stich, S. Speidel and S. Bodenstedt worked with medical device manufacturer KARL STORZ SE & Co. KG in the projects “InnOPlan”, funded by the German Federal Ministry of Economic Affairs and Energy (grant number BMWI 01MD15002E) and “Surgomics”, funded by the German Federal Ministry of Health (grant number BMG 2520DAT82D and BMG 2520DAT82A). Lars Mündermann is an employee of KARL STORZ SE & Co. KG. A. Reinke works with the Helmholtz Imaging Platform (HIP), a platform of the Helmholtz Incubator on Information and Data Science. S. Kondo was an employee of Konica Minolta Inc. when this work was done. Wolfgang Reiter is an employee of Wintegral GmbH, a subsidiary of medical device manufacturer Richard Wolf GmbH. I. Twick, K. Kirtac, E. Hosgor, J. Lindström Bolmgren, M. Stenzel and B. von Siemens are employees of Caresyntax GmbH. Felix Nickel received travel support for conference participation as well as equipment provided for laparoscopic surgery courses by KARL STORZ SE & Co. KG, Johnson & Johnson, Intuitive Surgical, Cambridge Medical Robotics, and Medtronic. The other authors have no conflicts of interest., (Copyright © 2023. Published by Elsevier B.V.)

Challenges have become the state-of-the-art approach to benchmark image analysis algorithms in a comparative manner. While the validation on identical data sets was a great step forward, results analysis is often restricted to pure ranking tables, leaving relevant questions unanswered. Specifically, little effort has been put into the systematic investigation on what characterizes images in which state-of-the-art algorithms fail. To address this gap in the literature, we (1) present a statistical framework for learning from challenges and (2) instantiate it for the specific task of instrument instance segmentation in laparoscopic videos. Our framework relies on the semantic meta data annotation of images, which serves as foundation for a General Linear Mixed Models (GLMM) analysis. Based on 51,542 meta data annotations performed on 2,728 images, we applied our approach to the results of the Robust Medical Instrument Segmentation Challenge (ROBUST-MIS) challenge 2019 and revealed underexposure, motion and occlusion of instruments as well as the presence of smoke or other objects in the background as major sources of algorithm failure. Our subsequent method development, tailored to the specific remaining issues, yielded a deep learning model with state-of-the-art overall performance and specific strengths in the processing of images in which previous methods tended to fail. Due to the objectivity and generic applicability of our approach, it could become a valuable tool for validation in the field of medical image analysis and beyond., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Part of this work was funded by the Helmholtz Imaging Platform (HIP), a platform of the Helmholtz Incubator on Information and Data Science and by the Surgical Oncology Program of the National Center for Tumor Diseases (NCT) Heidelberg., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Introduction: Esophagectomy is the mainstay of esophageal cancer treatment, but anastomotic insufficiency related morbidity and mortality remain challenging for patient outcome. Therefore, the objective of this work was to optimize anastomotic technique and gastric conduit perfusion with hyperspectral imaging (HSI) for total minimally invasive esophagectomy (MIE) with linear stapled anastomosis., Material and Methods: A live porcine model (n = 58) for MIE was used with gastric conduit formation and simulation of linear stapled side-to-side esophagogastrostomy. Four main experimental groups differed in stapling length (3 vs. 6 cm) and simulation of anastomotic position on the conduit (cranial vs. caudal). Tissue oxygenation around the anastomotic simulation site was evaluated using HSI and was validated with histopathology., Results: The tissue oxygenation (ΔStO 2 ) after the anastomotic simulation remained constant only for the short stapler in caudal position (-0.4 ± 4.4%, n.s.) while it was impaired markedly in the other groups (short-cranial: -15.6 ± 11.5%, p = 0.0002; long-cranial: -20.4 ± 7.6%, p = 0.0126; long-caudal: -16.1 ± 9.4%, p < 0.0001). Tissue samples from avascular stomach as measured by HSI showed correspondent eosinophilic pre-necrotic changes in 35.7 ± 9.7% of the surface area., Conclusion: Tissue oxygenation at the site of anastomotic simulation of the gastric conduit during MIE is influenced by stapling technique. Optimal oxygenation was achieved with a short stapler (3 cm) and sufficient distance of the simulated anastomosis to the cranial end of the gastric conduit. HSI tissue deoxygenation corresponded to histopathologic necrotic tissue changes. The experimental model with HSI and ML allow for systematic optimization of gastric conduit perfusion and anastomotic technique while clinical translation will have to be proven., Competing Interests: Declaration of competing interest The authors declare that there is no conflict of interest for the submission of the manuscript “Optimization of anastomotic technique and gastric conduit perfusion with hyperspectral imaging and machine learning in an experimental model for minimally invasive esophagectomy”., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Fluoroscopy-guided trauma and orthopedic surgeries involve the repeated acquisition of correct anatomy-specific standard projections for guidance, monitoring, and evaluating the surgical result. C-arm positioning is usually performed by hand, involving repeated or even continuous fluoroscopy at a cost of radiation exposure and time. We propose to automate this procedure and estimate the pose update for C-arm repositioning directly from a first X-ray without the need for a patient-specific computed tomography scan (CT) or additional technical equipment. Our method is trained on digitally reconstructed radiographs (DRRs) which uniquely provide ground truth labels for an arbitrary number of training examples. The simulated images are complemented with automatically generated segmentations, landmarks, and with simulated k-wires and screws. To successfully achieve a transfer from simulated to real X-rays, and also to increase the interpretability of results, the pipeline was designed to closely reflect the actual clinical decision-making process followed by spinal neurosurgeons. It explicitly incorporates steps such as region-of-interest (ROI) localization, detection of relevant and view-independent landmarks, and subsequent pose regression. The method was validated on a large human cadaver study simulating a real clinical scenario, including k-wires and screws. The proposed procedure obtained superior C-arm positioning accuracy of dθ=8.8°±4.2° average improvement (p t-test ≪0.01), robustness, and generalization capabilities compared to the state-of-the-art direct pose regression framework., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Lisa Kausch reports financial support was provided by Siemens Healthineers. Sarina Thomas, Jan El Babari, Eric Mandelka, Maxim Privalov, Sven Vetter, Klaus Maier-Hein reports financial support was provided by Siemens Healthineers., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Semantic image segmentation is an important prerequisite for context-awareness and autonomous robotics in surgery. The state of the art has focused on conventional RGB video data acquired during minimally invasive surgery, but full-scene semantic segmentation based on spectral imaging data and obtained during open surgery has received almost no attention to date. To address this gap in the literature, we are investigating the following research questions based on hyperspectral imaging (HSI) data of pigs acquired in an open surgery setting: (1) What is an adequate representation of HSI data for neural network-based fully automated organ segmentation, especially with respect to the spatial granularity of the data (pixels vs. superpixels vs. patches vs. full images)? (2) Is there a benefit of using HSI data compared to other modalities, namely RGB data and processed HSI data (e.g. tissue parameters like oxygenation), when performing semantic organ segmentation? According to a comprehensive validation study based on 506 HSI images from 20 pigs, annotated with a total of 19 classes, deep learning-based segmentation performance increases - consistently across modalities - with the spatial context of the input data. Unprocessed HSI data offers an advantage over RGB data or processed data from the camera provider, with the advantage increasing with decreasing size of the input to the neural network. Maximum performance (HSI applied to whole images) yielded a mean DSC of 0.90 ((standard deviation (SD)) 0.04), which is in the range of the inter-rater variability (DSC of 0.89 ((standard deviation (SD)) 0.07)). We conclude that HSI could become a powerful image modality for fully-automatic surgical scene understanding with many advantages over traditional imaging, including the ability to recover additional functional tissue information. Our code and pre-trained models are available at https://github.com/IMSY-DKFZ/htc., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The Authors declare that there is no conflict of interest., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

In computer vision, reference datasets from simulation and real outdoor scenes have been highly successful in promoting algorithmic development in stereo reconstruction. Endoscopic stereo reconstruction for surgical scenes gives rise to specific problems, including the lack of clear corner features, highly specular surface properties and the presence of blood and smoke. These issues present difficulties for both stereo reconstruction itself and also for standardised dataset production. Previous datasets have been produced using computed tomography (CT) or structured light reconstruction on phantom or ex vivo models. We present a stereo-endoscopic reconstruction validation dataset based on cone-beam CT (SERV-CT). Two ex vivo small porcine full torso cadavers were placed within the view of the endoscope with both the endoscope and target anatomy visible in the CT scan. Subsequent orientation of the endoscope was manually aligned to match the stereoscopic view and benchmark disparities, depths and occlusions are calculated. The requirement of a CT scan limited the number of stereo pairs to 8 from each ex vivo sample. For the second sample an RGB surface was acquired to aid alignment of smooth, featureless surfaces. Repeated manual alignments showed an RMS disparity accuracy of around 2 pixels and a depth accuracy of about 2 mm. A simplified reference dataset is provided consisting of endoscope image pairs with corresponding calibration, disparities, depths and occlusions covering the majority of the endoscopic image and a range of tissue types, including smooth specular surfaces, as well as significant variation of depth. We assessed the performance of various stereo algorithms from online available repositories. There is a significant variation between algorithms, highlighting some of the challenges of surgical endoscopic images. The SERV-CT dataset provides an easy to use stereoscopic validation for surgical applications with smooth reference disparities and depths covering the majority of the endoscopic image. This complements existing resources well and we hope will aid the development of surgical endoscopic anatomical reconstruction algorithms., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Odin Vision Ltd. Digital Surgery., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process., Competing Interests: Declaration of Competing Interest Anand Malpani is a future employee at Mimic Technologies Inc. (Seattle, WA, US). Johannes Fallert and Lars Mündermann are employed at KARL STORZ SE & Co. KG (Tuttlingen, Germany). Hirenkumar Nakawala is employed at CMR Surgical Ltd (Cambridge, UK). Nicolas Padoy is a scientific advisor of Caresyntax (Berlin, Germany). Daniel A. Hashimoto is a consultant for Johnson & Johnson (New Brunswick, NJ, USA), Verily Life Sciences (San Francisco, CA, USA), and Activ Surgical (Boston, MA, USA). He has received research support from Olympus Corporation and the Intuitive Foundation. Carla Pugh is the founder of 10 Newtons Inc. (Madison, WI, US). Danail Stoyanov is employed at Digital Surgery Ltd (London, UK) and Odin Vision Ltd (London, UK). Teodor Grantcharov is the founder of Surgical Safety Technologies Inc. (Toronto, Ontario, Canada). Tobias Roß is employed at Quality Match GmbH (Heidelberg, Germany). All other authors do not declare any conflicts of interest., (Copyright © 2021. Published by Elsevier B.V.)

Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization; algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts)., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020. Published by Elsevier B.V.)

The number of biomedical image analysis challenges organized per year is steadily increasing. These international competitions have the purpose of benchmarking algorithms on common data sets, typically to identify the best method for a given problem. Recent research, however, revealed that common practice related to challenge reporting does not allow for adequate interpretation and reproducibility of results. To address the discrepancy between the impact of challenges and the quality (control), the Biomedical Image Analysis ChallengeS (BIAS) initiative developed a set of recommendations for the reporting of challenges. The BIAS statement aims to improve the transparency of the reporting of a biomedical image analysis challenge regardless of field of application, image modality or task category assessed. This article describes how the BIAS statement was developed and presents a checklist which authors of biomedical image analysis challenges are encouraged to include in their submission when giving a paper on a challenge into review. The purpose of the checklist is to standardize and facilitate the review process and raise interpretability and reproducibility of challenge results by making relevant information explicit., Competing Interests: Declaration of Competing Interest Anne Martel is a co-founder, CSO of Pathcore, Toronto, Canada. The remaining authors declare no competing interests., (Copyright © 2020. Published by Elsevier B.V.)

The pierid butterfly Colias croceus (Geoffroy in Fourcroy, 1785), established in the Azores archipelago, is polymorphic with six forms, C. croceus f. croceus ♂ and ♀, C. c. f. cremonae ♂ and ♀, C. c. f. helice ♀, and C. c. f. cremonaehelice ♀. We investigated the optical mechanisms underlying the wing colouration of the butterflies by performing spectrophotometry and imaging scatterometry of the variously coloured wing areas and scales. The scale colouration is primarily due to wavelength-selective absorption of incident light by pterins expressed in granular beads in the wing scales, but thin film reflections of the scales' lower lamina and scale stacking also contribute. Three forms (croceus ♂ and ♀ and helice ♀) are consistent with the patterns of the well-known 'alba' polymorphism. We postulate the coexistence of a second polymorphism, 'cremonae', to understand the three other forms (cremonae ♂ and ♀, and cremonaehelice ♀), which are characterized by the absence of red pigment, presumably due to the differential blocking of erythropterin expression., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Worldwide, water resources are increasingly under pressure. The Water accounting approach of the System of Environmental-Economic Accounting (SEEA) has been developed to inform decision-makers on water supply, use, and quality. However, a critical issue in water accounting is finding data and models to populate SEEA water accounts. In particular, there are challenges in aligning hydrological models with accounting principles. Also, there is a need to test further how the SEEA water accounts can be connected to policy uses. The objective of this study is to develop water accounts with the use of a hydrological model. Specifically, we apply the SWAT hydrological model in the Buyuk Menderes Basin in Turkey to estimate the key hydrological parameters required for water accounting. To adapt and link SWAT to SEEA water accounts, we develop supply and use tables and asset accounts following the SEEA water for the year 2014 and explore how water accounts can inform policymaking. This article provides new insights into the added value of using a hydrological model in constructing water accounts for better water resources management., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Reticulated platelets (RP) are the youngest circulating platelets in blood. An increased amount of this subpopulation is associated with higher cardiovascular risk and mortality., Objectives: It is unknown to what extent intrinsic properties of RP contribute to their hyperreactive features. This study is the first providing a multifactorial approach based on ultrastructural, transcriptional, and functional analysis of RP compared to non-RP sorted by flow cytometry., Methods: Reticulated platelets and non-RP were sorted after platelet staining with SYTO 13. Employing transmission electron microscopy, 1089 micrographs were analyzed for platelet size, amounts of intracellular structures, and anatomical surrogates indicating activation. Long and small RNA-sequencing (RNA-seq) were performed for analyzing differential gene expression. Functional analysis of P-selectin-an upregulated mRNA in RP-was performed in healthy subjects and patients on P2Y 12 -inhibitors., Results: Electron micrographs uncovered distinct ultrastructural differences in RP versus non-RP. Cross sections were 1.9-fold larger in RP (P < .0001). Amounts of α-granules, dense granules, open canalicular system-openings, and mitochondria were increased in RP, which persisted after adjustment for platelet size. Long RNA-seq showed 1212 upregulated transcripts that are predominantly associated to platelet shape change, aggregation, and activation; 1264 mRNAs were downregulated in RP. Small RNA-seq did not reveal any differentially expressed transcripts. Functional analysis displayed higher P-selectin expression as compared to non-RP upon ADP- or TRAP-stimulation., Conclusions: Our results demonstrate that altered intrinsic structural and molecular properties contribute to the hyperreactivity of RP. These properties and an increased amount of RP may account for the association with cardiovascular risk., (© 2020 The Authors. Journal of Thrombosis and Haemostasis published by Wiley Periodicals LLC on behalf of International Society on Thrombosis and Haemostasis.)

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., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020. Published by Elsevier B.V.)

Vision-based tracking in an important component for building computer assisted interventions in minimally invasive surgery as it facilitates estimation of motion for instruments and anatomical targets. Tracking-by-detection algorithms are widely used for visual tracking, where the problem is treated as a classification task and a tracking target appearance model is updated over time using online learning. In challenging conditions, like surgical scenes, where tracking targets deform and vary in scale, the update step is prone to include background information in model appearance or to lack the ability to estimate change of scale, which degrades the performance of classifier. In this paper, we propose a Patch-based Adaptive Weighting with Segmentation and Scale (PAWSS) tracking framework that tackles both scale and background problems. A simple but effective colour-based segmentation model is used to suppress background information and multi-scale samples are extracted to enrich the training pool, which allows the tracker to handle both incremental and abrupt scale variations between frames. Experimentally, we evaluate our approach on Online Tracking Benchmark (OTB) dataset and Visual Object Tracking (VOT) challenge datasets, showing that our approach outperforms recent state-of-the-art trackers, and it especially improves successful rate score on OTB dataset, while on VOT datasets, PAWSS ranks among the top trackers while operating at real-time frame rates. Focusing on the application of PAWSS to surgical scenes, we evaluate on MICCAI 2015 challenge instrument tracking challenge and in vivo datasets, showing that our approach performs the best among all submitted methods and also has promising performance on in vivo surgical instrument tracking., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

Surgical guidance and decision making could be improved with accurate and real-time measurement of intra-operative data including shape and spectral information of the tissue surface. In this work, a dual-modality endoscopic system has been proposed to enable tissue surface shape reconstruction and hyperspectral imaging (HSI). This system centers around a probe comprised of an incoherent fiber bundle, whose fiber arrangement is different at the two ends, and miniature imaging optics. For 3D reconstruction with structured light (SL), a light pattern formed of randomly distributed spots with different colors is projected onto the tissue surface, creating artificial texture. Pattern decoding with a Convolutional Neural Network (CNN) model and a customized feature descriptor enables real-time 3D surface reconstruction at approximately 12 frames per second (FPS). In HSI mode, spatially sparse hyperspectral signals from the tissue surface can be captured with a slit hyperspectral imager in a single snapshot. A CNN based super-resolution model, namely "super-spectral-resolution" network (SSRNet), has also been developed to estimate pixel-level dense hypercubes from the endoscope cameras standard RGB images and the sparse hyperspectral signals, at approximately 2 FPS. The probe, with a 2.1 mm diameter, enables the system to be used with endoscope working channels. Furthermore, since data acquisition in both modes can be accomplished in one snapshot, operation of this system in clinical applications is minimally affected by tissue surface movement and deformation. The whole apparatus has been validated on phantoms and tissue (ex vivo and in vivo), while initial measurements on patients during laryngeal surgery show its potential in real-world clinical applications., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Background: Animal models of serious infection suggest that 24 h of induced hypothermia improves circulatory and respiratory function and reduces mortality. We tested the hypothesis that a reduction of core temperature to 32-34°C attenuates organ dysfunction and reduces mortality in ventilator-dependent patients with septic shock., Methods: In this randomised, controlled, open-label trial, we recruited patients from ten intensive care units (ICUs) in three countries in Europe and North America. Inclusion criteria for patients with severe sepsis or septic shock were a mean arterial pressure of less than 70 mm Hg, mechanical ventilation in an ICU, age at least 50 years, predicted length of stay in the ICU at least 24 h, and recruitment into the study within 6 h of fulfilling inclusion criteria. Exclusion criteria were uncontrolled bleeding, clinically important bleeding disorder, recent open surgery, pregnancy or breastfeeding, or involuntary psychiatric admission. We randomly allocated patients 1:1 (with variable block sizes ranging from four to eight; stratified by predictors of mortality, age, Acute Physiology and Chronic Health Evaluation II score, and study site) to routine thermal management or 24 h of induced hypothermia (target 32-34°C) followed by 48 h of normothermia (36-38°C). The primary endpoint was 30 day all-cause mortality in the modified intention-to-treat population (all randomly allocated patients except those for whom consent was withdrawn or who were discovered to meet an exclusion criterion after randomisation but before receiving the trial intervention). Patients and health-care professionals giving the intervention were not masked to treatment allocation, but assessors of the primary outcome were. This trial is registered with ClinicalTrials.gov, number NCT01455116., Findings: Between Nov 1, 2011, and Nov 4, 2016, we screened 5695 patients. After recruitment of 436 of the planned 560 participants, the trial was terminated for futility (220 [50%] randomly allocated to hypothermia and 216 [50%] to routine thermal management). In the hypothermia group, 96 (44·2%) of 217 died within 30 days versus 77 (35·8%) of 215 in the routine thermal management group (difference 8·4% [95% CI -0·8 to 17·6]; relative risk 1·2 [1·0-1·6]; p=0·07])., Interpretation: Among patients with septic shock and ventilator-dependent respiratory failure, induced hypothermia does not reduce mortality. Induced hypothermia should not be used in patients with septic shock., Funding: Trygfonden, Lundbeckfonden, and the Danish National Research Foundation., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Meeting the dual objectives of food security and ecosystem protection is a major challenge in sub-Saharan Africa (SSA). To this end agricultural intensification is considered desirable, yet, there remain uncertainties regarding the impact of climate change on opportunities for agricultural intensification and the adequacy of intensification options given the rapid population growth. We quantify trade-offs between levels of yield gap closure, food availability and forest and woodland conservation under different scenarios. Each scenario is made up of a combination of variants of four parameters i.e. (1) climate change based on Representative Concentration Pathways (RCPs); (2) population growth based on Shared Socioeconomic Pathways (SSPs); (3) cropland expansion with varying degrees of deforestation; and (4) different degrees of yield gap closure. We carry out these analyses for three major food crops, i.e. maize, cassava and yam, in Benin. Our analyses show that in most of the scenarios, the required levels of yield gap closures required to maintain the current levels of food availability can be achieved by 2050 by maintaining the average rate of yield increases recorded over the past two and half decades in addition to the current cropping intensity. However, yields will have to increase at a faster rate than has been recorded over the past two and half decades in order to achieve the required levels of yield gap closures by 2100. Our analyses also show that without the stated levels of yield gap closure, the areas under maize, cassava and yam cultivation will have to increase by 95%, 102% and 250% respectively in order to maintain the current levels of per capita food availability. Our study shows that food security outcomes and forest and woodland conservation goals in Benin and likely the larger SSA region are inextricably linked together and require holistic management strategies that considers trade-offs and co-benefits., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Background: Depression is frequently associated with chronic pain or chronic stress. Among cortical areas, the anterior cingulate cortex (ACC, areas 24a and 24b) appears to be important for mood disorders and constitutes a neuroanatomical substrate for investigating the underlying molecular mechanisms. The current work aimed at identifying ACC molecular factors subserving depression., Methods: Anxiodepressive-like behaviors in C57BL/6J male mice were induced by neuropathic pain, unpredictable chronic mild stress, and optogenetic ACC stimulation and were evaluated using novelty suppressed feeding, splash, and forced swim tests. ACC molecular changes in chronic pain-induced depression were uncovered through whole-genome expression analysis. Further mechanistic insights were provided by chromatin immunoprecipitation, Western blot, and immunostaining. The causal link between molecular changes and depression was studied using knockout, pharmacological antagonism, and local viral-mediated gene knockdown., Results: Under chronic pain-induced depression, gene expression changes in the ACC highlighted the overexpression of a regulator of the mitogen-activated protein kinase pathway, mitogen-activated protein kinase phosphatase-1 (MKP-1). This upregulation is associated with the presence of transcriptionally active chromatin marks (acetylation) at its proximal promoter region as well as increased cyclic adenosine monophosphate response element-mediated transcriptional activity and phosphorylation of cyclic adenosine monophosphate response element binding protein and activating transcription factor. MKP-1 overexpression is also observed with unpredictable chronic mild stress and repeated ACC optogenetic stimulation and is reversed by fluoxetine. A knockout, an antagonist, or a local silencing of MKP-1 attenuates depressive-like behaviors, pointing to an important role of this phosphatase in depression., Conclusions: These data point to ACC MKP-1 as a key factor in the pathophysiology of depression and a potential target for treatment development., (Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Indocyanine green (ICG) is a fluorescent dye that has been widely used for fluorescence imaging during hepatobiliary surgery. ICG is injected intravenously, selectively taken up by the liver, and then secreted into the bile. The catabolism and fluorescence properties of ICG permit a wide range of visualization methods in hepatobiliary surgery. We have characterized the applications of ICG during hepatobiliary surgery into: 1) liver mapping, 2) cholangiography, 3) tumor visualization, and 4) partial liver graft evaluation. In this literature review, we summarize the current understanding of ICG use during hepatobiliary surgery. Intra-operative ICG fluorescence imaging is a safe, simple, and feasible method that improves the visualization of hepatobiliary anatomy and liver tumors. Intravenous administration of ICG is not toxic and avoids the drawbacks of conventional imaging. In addition, it reduces post-operative complications without any known side effects. ICG fluorescence imaging provides a safe and reliable contrast for extra-hepatic cholangiography when detecting intra-hepatic bile leakage following liver resection. In addition, liver tumors can be visualized and well-differentiated hepatocellular carcinoma tumors can be accurately identified. Moreover, vascular reconstruction and outflow can be evaluated following partial liver transplantation. However, since tissue penetration is limited to 5-10mm, deeper tissue cannot be visualized using this method. Many instances of false positive or negative results have been reported, therefore further characterization is required., (Copyright © 2016 Elsevier B.V. All rights reserved.)

In this paper, we propose a multi-sensor super-resolution framework for hybrid imaging to super-resolve data from one modality by taking advantage of additional guidance images of a complementary modality. This concept is applied to hybrid 3-D range imaging in image-guided surgery, where high-quality photometric data is exploited to enhance range images of low spatial resolution. We formulate super-resolution based on the maximum a-posteriori (MAP) principle and reconstruct high-resolution range data from multiple low-resolution frames and complementary photometric information. Robust motion estimation as required for super-resolution is performed on photometric data to derive displacement fields of subpixel accuracy for the associated range images. For improved reconstruction of depth discontinuities, a novel adaptive regularizer exploiting correlations between both modalities is embedded to MAP estimation. We evaluated our method on synthetic data as well as ex-vivo images in open surgery and endoscopy. The proposed multi-sensor framework improves the peak signal-to-noise ratio by 2 dB and structural similarity by 0.03 on average compared to conventional single-sensor approaches. In ex-vivo experiments on porcine organs, our method achieves substantial improvements in terms of depth discontinuity reconstruction., (Copyright © 2015 Elsevier B.V. All rights reserved.)

One of the main challenges in computer-assisted soft tissue surgery is the registration of multi-modal patient-specific data for enhancing the surgeon's navigation capabilities by observing beyond exposed tissue surfaces. A new approach to marker-less guidance involves capturing the intra-operative patient anatomy with a range image device and doing a shape-based registration. However, as the target organ is only partially visible, typically does not provide salient features and underlies severe non-rigid deformations, surface matching in this context is extremely challenging. Furthermore, the intra-operatively acquired surface data may be subject to severe systematic errors and noise. To address these issues, we propose a new approach to establishing surface correspondences, which can be used to initialize fine surface matching algorithms in the context of intra-operative shape-based registration. Our method does not require any prior knowledge on the relative poses of the input surfaces to each other, does not rely on the detection of prominent surface features, is robust to noise and can be used for overlapping surfaces. It takes into account (1) similarity of feature descriptors, (2) compatibility of multiple correspondence pairs, as well as (3) the spatial configuration of the entire correspondence set. We evaluate the algorithm on time-of-flight (ToF) data from porcine livers in a respiratory liver motion simulator. In all our experiments the alignment computed from the established surface correspondences yields a registration error below 1cm and is thus well suited for initializing fine surface matching algorithms for intra-operative soft-tissue registration., (Copyright © 2014 Elsevier B.V. All rights reserved.)

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative 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 capabilities by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. 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 optical imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Background/objectives: We examined the applicability of contrast-enhanced ultrasound (CEUS) for imaging of murine deep vein thrombosis (DVT) and measured the effects of enoxaparin, ticagrelor and P2Y(12) receptor deficiency in vivo., Methods: Deep vein thrombosis was induced by exposure to ferric chloride or ligation of the infrarenal vena cava of C57BL/6 mice after pretreatment with enoxaparin, ticagrelor or vehicle and in P2Y(12-/-) mice. Initial thrombus growth was visualized by intravital microscopy. Thrombi were weighed and examined by immunohistochemistry. CEUS was performed with a standard ultrasound system (Vivid 7, GE Healthcare) in the open abdominal cavity after injection of stabilized sulphur hexafluoride microbubbles., Results: Incubation with ferric chloride resulted in non-occluding platelet-containing thrombus growth within 15-25 min. Sham-operated mice, enoxaparin- and ticagrelor-pretreated wild-type and P2Y(12-/-) mice developed only small thrombi. After injection of the contrast agent, growing thrombi were delineated clearly as negative contrast on CEUS. Thrombus size on CEUS after 25 min was significantly smaller in enoxaparin- (0.3 ± 0.1 mm(2)) and ticagrelor-treated (0.5 ± 0.1 mm(2)) wild-type and in P2Y(12-/-) mice (0.4 ± 0.1 mm(2)) as compared with vehicle-treated wild-type mice (2.0 ± 0.3 mm(2)) in the maximal sagittal plane (P < 0.001, n = 5-10). CEUS-derived thrombus size correlated linearly with thrombus weight and also reflected the extent of ligation-induced DVT., Conclusions: Contrast-enhanced ultrasound allowed the real-time quantification of DVT in living mice. Genetic and pharmacologic antithrombotic interventions were well reflected by CEUS and suggested an important role of the platelet P2Y(12) receptor in early DVT formation., (© 2013 International Society on Thrombosis and Haemostasis.)

Angiotensin II (Ang II) activates at least two receptors, AT1 and AT2, with the majority of its effects-such as vasoconstriction, inflammation, and matrix deposition-mediated by the AT1 receptor. It is thought that the AT2 receptor counteracts these processes; however, recent studies have found proinflammatory and hypertrophic effects of this receptor subtype. To identify the physiological roles of the AT2 receptor in chronic kidney disease, we performed renal ablation in AT2 receptor knockout and wild-type mice. Renal injury caused a greater impairment of renal function, glomerular injury, albuminuria, and mortality in the knockout mice than in the wild-type mice. There was increased fibronectin expression and inflammation in the knockout mice, as shown by augmented monocyte/macrophage infiltration and higher chemokine monocyte chemotactic protein-1 (MCP-1) and RANTES expression in the remnant kidney. The higher mortality and renal morbidity of the knockout mice was not due to differences in systemic blood pressure, glomerular volume, AT1 receptor, renin, or endothelial nitric oxide synthase expression. Whether activation of the AT2 receptor will have therapeutic benefit in chronic kidney disease will require further study.

Platelet activation plays a central role in hemostasis and thrombosis. Many platelet agonists function through G-protein-coupled receptors. Epinephrine activates the alpha(2A)-adrenergic receptor (alpha(2A)) that couples to G(z) in platelets. Although alpha(2A) was originally cloned from platelets, its role in thrombosis and hemostasis is still unclear. Through analysis of alpha(2A)-deficient mice, variable tail bleeding times were observed. In vitro, epinephrine potentiated activation/aggregation responses of wild-type but not alpha(2A)-deficient platelets as determined by flow cytometry and aggregometry, whereas perfusion studies showed no differences in platelet adhesion and thrombus formation on collagen. To test the in vivo relevance of alpha(2A) deficiency, mice were subjected to 3 different thrombosis models. As expected, alpha(2A)-deficient mice were largely protected from lethal pulmonary thromboembolism induced by the infusion of collagen/epinephrine. In a model of FeCl(3)-induced injury in mesenteric arterioles, alpha(2A)(-/-) mice displayed a 2-fold increase in embolus formation, suggesting thrombus instability. In a third model, the aorta was mechanically injured, and blood flow was measured with an ultrasonic flow probe. In wild-type mice, all vessels occluded irreversibly, whereas in 24% of alpha(2A)-deficient mice, the initially formed thrombi embolized and blood flow was reestablished. These results demonstrate that alpha(2A) plays a significant role in thrombus stabilization.

We report the existence of two structurally distinct forms of the angiotensin receptor AT-1 in the mouse. A Balb/c mouse genomic library was screened by homology screening with a polymerase chain reaction (PCR) amplified probe. Restriction mapping and sequencing of the isolated genes revealed the presence of two receptor isoforms, here named the mouse AT-1a and AT-1b receptors, containing 22 different amino acids. Receptor binding studies performed on COS-7 cells transfected with the two receptors revealed that they had similar binding profiles for angiotensin II, angiotensin III and AT-1 or AT-2 specific antagonists. Because many of the structural differences were in the carboxy terminal putative intracellular domain, we speculate that these isoforms may differ in their regulation, signal transduction, or desensitization mechanisms.

The experimental immunomodulatory agent tilorone was previously reported to induce generalized mucopolysaccharidosis (lysosomal storage of sulfated glycosaminoglycans) and lipidosis in rats. While lipidosis is a side effect common to many cationic amphiphilic compounds, none of them except tilorone has been known to cause mucopolysaccharidosis in intact animals. The purpose of the present histochemical and ultrastructural study was to examine whether or not mucopolysaccharidosis can be induced by tilorone analogues. Three analogues were selected and administered to rats in short-term and subchronic experiments, and liver, spleen, kidney, and cornea were examined. The analogues caused generalized cellular lesions which had the same histochemical and cytological characteristics and the same distribution as the lesions produced by tilorone. The results show that the ability to induce mucopolysaccharidosis is not a unique property of tilorone; this drug side effect should be taken into account when developing new drugs with molecular structures resembling that of tilorone.