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In this paper, we study the indirect relationship between the adoption of Computer-aided Detection or Diagnostic (CADe or CADx) systems for Hepatic Resection (HR) and the patient’s health post-surgery. We vary the number, actual size, and the estimated size of tumors along with model parameters of tumor growth over 1000 simulations of HR according to predefined statistical distributions of parameter values. The average time (t) taken by the tumors to relapse is assessed for the non-adoption of CAD (Case 1), the adoption of semi-automatic CAD (Case 2) and the adoption of automatic CAD (Case 3) in HR. In this study, we have simulated 126 automatic CAD algorithms (Case 3). For tumor volumes (TV) less than 50 cm, if administration of bevacizumab, a post-operative therapy, is (not) adopted in the simulation, t is found to be 646, 84, and 60 days (40, 24, and 17 days) for Case 1, Case 2, and Case 3, respectively. For TV greater than 50 cm, and with (without) bevacizumab, t is found to be 86, 1, and 6 days (28, 6, and 3 days) for Case 1, Case 2, and Case 3, respectively. For with (without) bevacizumab treatment and for all tumor volumes, t is found to be 260, 90, and 104 days (38, 13, and 11 days) for Case 1, Case 2, and Case 3, respectively. We have observed that the tumors relapsed quickly in those cases where CAD was adopted.

Image segmentation being the first step is always crucial for brain aneurysm treatment planning; it is also crucial during the procedure. A robust brain aneurysm segmentation has the potential to prevent the blood leakage, also known as sentinel hemorrhage. Here, we present a method combining a multiresolution and a statistical approach in two dimensional domain to segment cerebral aneurysm in which the Contourlet transform (CT) extracts the image features, while the Hidden Markov Random Field with Expectation Maximization (HMRF-EM) segments the image, based on the spatial contextual constraints. The proposed algorithm is tested on Three-Dimensional Rotational Angiography (3DRA) datasets; the average values of segmentation accuracy, DSC, FPR, FNR, specificity, and sensitivity, are found to be 99.72%, 93.52%, 0.07%, 5.23%, 94.77%, and 99.96%, respectively.

Liver Ultrasound (US) or sonography is popularly used because of its real-time output, low-cost, ease-of-use, portability, and non-invasive nature. Segmentation of real-time liver US is essential for diagnosing and analyzing liver conditions (e.g., hepatocellular carcinoma (HCC)), assisting the surgeons/radiologists in therapeutic procedures. In this paper, we propose a method using a modified Pyramid Scene Parsing (PSP) module in tuned neural network backbones to achieve real-time segmentation without compromising the segmentation accuracy. Considering widespread noise in US data and its impact on outcomes, we study the impact of pre-processing and the influence of loss functions on segmentation performance. We have tested our method after annotating a publicly available US dataset containing 2400 images of 8 healthy volunteers (link to the annotated dataset is provided); the results show that the Dense-PSP-UNet model achieves a high Dice coefficient of 0.913±0.024 while delivering a real-time performance of 37 frames per second (FPS).

Introduction: Percutaneous thermal ablation is widely adopted as a curative treatment approach for unresectable liver neoplasms. Accurate immediate assessment of therapeutic response post-ablation is critical to achieve favourable outcomes. The conventional technique of side-by-side comparison of pre- and post-ablation scans is challenging and hence there is a need for improved methods, which will accurately evaluate the immediate post-therapeutic response. Objectives and Significance: This review summarizes the findings of studies investigating the feasibility and efficacy of the fusion imaging systems in the immediate post-operative assessment of the therapeutic response to thermal ablation in liver neoplasms. The findings could potentially empower the clinicians with updated knowledge of the state-of-the-art in the assessment of treatment response for unresectable liver neoplasms. Methods and Analysis: A rapid review will be performed on publicly available major electronic databases to identify articles reporting the feasibility and efficacy of the fusion imaging systems in the immediate assessment of the therapeutic response to thermal ablation in liver neoplasms. The risk of bias and quality of articles will be assessed using the Cochrane risk of bias tool 2.0 and Newcastle Ottawa tool. Ethics and Dissemination: Being a review, we do not anticipate the need for any approval from the Institutional Review Board. The outcomes of this study will be published in a peer-reviewed journal. Highlights Evaluation of the therapeutic response in liver neoplasms immediately post-ablation is critical to achieve favourable patient outcomes. We will examine the feasibility and technical efficacy of different fusion imaging systems in assessing the immediate treatment response post-ablation. The findings are expected to guide the clinicians with updated knowledge on the state-of-the-art when assessing the immediate treatment response for unresectable liver neoplasms.

Medical images (e.g., magnetic resonance imaging (MRI) and computed tomography (CT)) provide critical information to the clinicians in order to diagnose pathology and plan interventions. Image segmentation is the first and foremost step taken by the clinicians while optimizing analytic diagnosis and treatment planning for interventions (e.g., transplantation and complete resection) and therapeutic procedures (e.g., radiotherapy, PVE, and embolization approaches), especially in hepatocellular carcinoma. Thus, segmentation techniques certainly impact the diagnosis and treatment outcomes. This paper studies the literature during the year 2012 until 2021 and reviews the segmentation methods classifying them into three categories based on their clinical utility (i.e., surgical and radiological interventions). The classification is based on the parameters such as precision, accuracy, location, liver condition, and other clinical considerations.

The inhalation toxicology of multifaceted particulate matter from the environment, cigarette smoke, and e-cigarette liquid vapes is a major research topic concerning the adverse effect of these items on lung tissue. In vitro air-liquid interface (ALI) culture models hold more potential in an inhalation toxicity assessment. Apropos to e-cigarette toxicity, the multiflavor components of the vapes pose a complex experimental bottleneck. While an appropriate ALI setup has been one part of the focus to overcome this, parallel attention towards the development of an ideal exposure system has pushed the field forward. With the advent of microfluidic devices, lung-on-chip (LOC) technologies show enormous opportunities in in vitro smoke-related inhalation toxicity. In this review, we provide a framework, establish a paradigm about smoke-related inhalation toxicity testing in vitro, and give a brief overview of breathing LOC experimental design concepts. The capabilities with optimized bioengineering approaches and microfluidics and their fundamental pros and cons are presented with specific case studies. The LOC model can imitate the structural, functional, and mechanical properties of human alveolar-capillary interface and are more reliable than conventional in vitro models. Finally, we outline current perspective challenges as well as opportunities of future development to smoking lungs-on-chip technologies based on advances in soft robotics, machine learning, and bioengineering.

The blood-brain barrier (BBB) is a prime focus for clinicians to maintain the homeostatic function in health and deliver the theranostics in brain cancer and number of neurological diseases. The structural hierarchy and in situ biochemical signaling of BBB neurovascular unit have been primary targets to recapitulate into the in vitro modules. The microengineered perfusion systems and development in 3D cellular and organoid culture have given a major thrust to BBB research for neuropharmacology. In this review, we focus on revisiting the nanoparticles based bimolecular engineering to enable them to maneuver, control, target, and deliver the theranostic payloads across cellular BBB as nanorobots or nanobots. Subsequently we provide a brief outline of specific case studies addressing the payload delivery in brain tumor and neurological disorders (e.g., Alzheimer's disease, Parkinson's disease, multiple sclerosis, etc.). In addition, we also address the opportunities and challenges across the nanorobots' development and design. Finally, we address how computationally powered machine learning (ML) tools and artificial intelligence (AI) can be partnered with robotics to predict and design the next generation nanorobots to interact and deliver across the BBB without causing damage, toxicity, or malfunctions. The content of this review could be references to multidisciplinary science to clinicians, roboticists, chemists, and bioengineers involved in cutting-edge pharmaceutical design and BBB research.

Introduction: Almost two million people worldwide die annually due to hepatic-related diseases. Half of these diseases are attributed to cirrhosis and the other half are related to hepatitis and hepatocellular carcinoma (HCC). The liver is also a metastasis hub from adjacent organs. This research aims to create an accurate high-quality delineation of the human liver and prepare them to be 3D printed for medical analysis to help aid medical practitioners in pre-procedural planning. Materials and Methods: Convolutional neural networks (ConvNets) are used to perform the liver tissues delineation. A famous ConvNet, named U-net, is used as the basis benchmark architecture that is also known for its great outcomes in the medical segmentation field. Contrast-enhanced computerized tomography (CT) scans are used from the famous Medical Segmentation Decathlon Challenge (Task 8: Hepatic Vessel), abbreviated as MSDC-T8. It contains 443 CT scans, which is considered the largest dataset that contains both the tumors and vessels ground-truth segmentation. Some researchers also generated the liver masks for this dataset, making it a complete dataset that contains all the relevant tissues’ ground-truth masks. Results: Currently, the liver delineation has been successfully done with very high DICE = 98.12% (higher than the state-of-the-art results DICE = 97.61%), where a comparison between two famous schedulers namely, ReduceLRonPlateau and OneCycleLR has been conducted. Moreover, the 3D liver volume creation has also been completed and built via the marching cube algorithm. Conclusions/Future Directions: The developed ConvNet can segment livers with high confidence. The tumor(s) and vessels tissues segmentation are also under investigation now. Moreover, newly devised self-organized neural networks (Self-ONN) look promising and will be investigated soon. Lastly, a GUI will be built so that the medical practitioner can just insert the CT volume and get the 3D liver volume with all the segmented tissues.

Accurate organ segmentation is nowadays considered indispensable in typical surgical pipelines. Over the last couple of decades, researchers and clinicians have been driven to achieve the most efficient segmentation technique that would allow radiologists and surgeons to have easy access to organ measurements and visualization. Although several implementations and systems have been implemented, there still lies plenty of space in creating a more robust, accurate, reliable, and fast segmentation algorithm. Since automated segmentation algorithms have less user interaction (minimizing the probability of performance variation) and are usually preferred by the clinicians, the aim of this paper is to provide a state-of-art automated solution for accurately segmenting the liver and liver tumor from CT volumes. The implementation utilizes a deep learning hybrid network that blends features extracted from both 2D and 3D convolutions to enhance the overall performance of the algorithm, in addition to some pre-processing and postprocessing. We tested this network on both public and private data and it showed promising results that would be highly applicable in surgical setups. Finally, we briefly discuss how these results are relevant in a typical surgical application.

To provide an overview of the novel applications of Augmented Reality (AR) and Virtual Reality (VR) technologies within the realm of Surgery. This chapter will clarify the concepts associated with AR and VR in the context of surgery, and address implementation approaches of AR and VR in surgical pre-operative planning and surgical training. Some examples of state-of-the-art solutions and system architecture developed by the editors will be described and discussed.

Respiratory toxicology remains a major research area in the 21st century since current scenario of airborne viral infection transmission and pollutant inhalation is expected to raise the annual morbidity beyond 2 million. Clinical and epidemiological research connecting human exposure to air contaminants to understand adverse pulmonary health outcomes is, therefore, an immediate subject of human health assessment. Important observations in defining systemic effects of environmental contaminants on inhalation metabolic dysfunction, liver health, and gastrointestinal tract have been well explored with in vivo models. In this review, a framework is provided, a paradigm is established about inhalation toxicity testing in vitro, and a brief overview of breathing Lungs-on-Chip (LoC) as design concepts is given. The optimized bioengineering approaches and microfluidics with their fundamental pros, and cons are presented. There are different strategies that researchers apply to inhalation toxicity studies to assess a variety of inhalable substances and relevant LoC approaches. A case study from published literature and frame arguments about reproducibility as well as in vitro/in vivo correlations are discussed. Finally, the opportunities and challenges in soft robotics, systems inhalation toxicology approach integrating bioengineering, machine learning, and artificial intelligence to address a multitude model for future toxicology are discussed.

Quantitative evaluation of cardiac function from magnetic resonance images generally requires the clinician to first trace the left ventricle contours. However, detection of myocardial walls continues to remain a challenge on magnetic resonance images acquired from patients having serious pathologies because of three key issues: 1) low contrast; 2) high noise level; and 3) blood pool region in the left ventricle is highly non-homogeneous. In this paper, a semi-automatic graph-based method is proposed to segment such pathological left ventricles. This paper has mainly three contributions, and they are: 1) weighting function in graph-based approaches for image segmentation is thoroughly analyzed; 2) a new weighting function is introduced for graph-based methods to outline the endocardium; and 3) epicardium is extracted by a proposed active contour model. We have tested the algorithm on real data sets obtained from two sources, Hospital for Sick Children (SICK-KID), Toronto, and MICCAI Left Ventricle Segmentation Challenge. Average Dice coefficients (in %), false positive ratio, false negative ratio, sensitivity, and specificity for SICK-KID database are found to be 94.7 ± 1.1, 0.023, $8.13\times 10^{-3}, 0.93$ , and 0.76, respectively; for MICCAI database, they are ${96.1\pm 1.1,} 0.022, 8.10\times 10^{-3}, 0.94$ , and 0.74, respectively. Average Hausdorff distances between segmented contour and ground truth in these two databases are determined to be 2.86 and 2.84 mm, respectively. Promising experimental results by the method tested on publicly available database demonstrate the potential of the approach.

Cerebral aneurysm is a cerebrovascular disorder caused by a weakness in the wall of an artery or vein, which causes a localized dilation or ballooning of the blood vessel. It is life-threatening; hence, an early and accurate diagnosis would be a great aid to medical professionals in making the correct choice of treatment. HemeLB is a massively parallel Lattice-Boltzmann simulation software, which is designed to provide the radiologist with estimates of flow rates, pressures, and shear stresses throughout the relevant vascular structures, intended to eventually permit greater precision in the choice of therapeutic intervention. However, in order to allow surgeries and doctors to view and visualize the results in real-time at medical environments, a cost-efficient, practical platform is needed. In this article, we have developed and evaluated a version of HemeLB on various heterogeneous system-on-chip platforms, allowing users to run HemeLB on a low-cost embedded platform and to visualize the simulation results in real-time. A comprehensive evaluation of implementation on the Zynq SoC and Jetson TX1 embedded graphic processing unit platforms are reported. The achieved results show that the proposed Jetson TX1 implementation outperforms the Zynq implementation by a factor of 19 in terms of site updates per second. 2007-2012 IEEE. Manuscript received February 19, 2019; revised September 20, 2019; accepted November 4, 2019. Date of publication December 2, 2019; date of current version June 3, 2020. This paper was supported by the National Priorities Research Program (NPRP) under Grant 5-792-2-328 from the Qatar National Research Fund (a member of Qatar Foundation). X. Zhai is partially supported by the UK Engineering and Physical Sciences Research Council under Grants EP/R02572X/1 and EP/P017487/1. (Corresponding author: Xiaojun Zhai.) X. Zhai is with the School of Computer Science and Electronic Engineering, University of Essex, CO4 3SQ Colchester, U.K. (e-mail: xzhai@essex.ac.uk). Scopus

Purpose: Cerebral aneurysms are one of the prevalent cerebrovascular disorders in adults worldwide and caused by a weakness in the brain artery. The most impressive treatment for a brain aneurysm is interventional radiology treatment, which is extremely dependent on the skill level of the radiologist. Hence, accurate detection and effective therapy for cerebral aneurysms still remain important clinical challenges. In this work, we have introduced a pipeline for cerebral blood flow simulation and real-time visualization incorporating all aspects from medical image acquisition to real-time visualization and steering. Methods: We have developed and employed an improved version of HemeLB as the main computational core of the pipeline. HemeLB is a massive parallel lattice-Boltzmann fluid solver optimized for sparse and complex geometries. The visualization component of this pipeline is based on the ray marching method implemented on CUDA capable GPU cores. Results: The proposed visualization engine is evaluated comprehensively and the reported results demonstrate that it achieves significantly higher scalability and sites updates per second, indicating higher update rate of geometry sites' values, in comparison with the original HemeLB. This proposed engine is more than two times faster and capable of 3D visualization of the results by processing more than 30 frames per second. Conclusion: A reliable modeling and visualizing environment for measuring and displaying blood flow patterns in vivo, which can provide insight into the hemodynamic characteristics of cerebral aneurysms, is presented in this work. This pipeline increases the speed of visualization and maximizes the performance of the processing units to do the tasks by breaking them into smaller tasks and working with GPU to render the images. Hence, the proposed pipeline can be applied as part of clinical routines to provide the clinicians with the real-time cerebral blood flow-related information. 2020, CARS. This study was made possible by a National Priorities Research Program (NPRP) Grant No. 5-792-2-328 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

Background and objectives Surgical procedures such as laparoscopic and robotic surgeries are popular since they are invasive in nature and use miniaturized surgical instruments for small incisions. Tracking of the instruments (graspers, needle drivers) and field of view from the stereoscopic camera during surgery could further help the surgeons to remain focussed and reduce the probability of committing any mistakes. Tracking is usually preferred in computerized video surveillance, traffic monitoring, military surveillance system, and vehicle navigation. Despite the numerous efforts over the last few years, object tracking still remains an open research problem, mainly due to motion blur, image noise, lack of image texture, and occlusion. Most of the existing object tracking methods are time-consuming and less accurate when the input video contains high volume of information and more number of instruments. Methods This paper presents a variational framework to track the motion of moving objects in surgery videos. The key contributions are as follows: (1) A denoising method using stochastic resonance in maximal overlap discrete wavelet transform is proposed and (2) a robust energy functional based on Bhattacharyya coefficient to match the target region in the first frame of the input sequence with the subsequent frames using a similarity metric is developed. A modified affine transformation-based registration is used to estimate the motion of the features following an active contour-based segmentation method to converge the contour resulted from the registration process. Results and conclusion The proposed method has been implemented on publicly available databases; the results are found satisfactory. Overlap index (OI) is used to evaluate the tracking performance, and the maximum OI is found to be 76% and 88% on private data and public data sequences.

A weakness in the wall of a cerebral artery causing a dilation or ballooning of the blood vessel is known as a cerebral aneurysm. Optimal treatment requires fast and accurate diagnosis of the aneurysm. HemeLB is a fluid dynamics solver for complex geometries developed to provide neurosurgeons with information related to the flow of blood in and around aneurysms. On a cost efficient platform, HemeLB could be employed in hospitals to provide surgeons with the simulation results in real-time. In this work, we developed an improved version of HemeLB for GPU implementation and result visualization. A visualization platform for smooth interaction with end users is also presented. Finally, a comprehensive evaluation of this implementation is reported. The results demonstrate that the proposed implementation achieves a maximum performance of 15,168,964 site updates per second, and is capable of speeding up HemeLB for deployment in hospitals and clinical investigations. - 2019 IEEE. This paper was made possible by National Priorities Research Program (NPRP) grant No. 5-792-2-328 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

Cerebral aneurysms are among most prevalent and devastating cerebrovascular diseases of adult population worldwide. The resulting sequelae of untimely/inadequate therapeutic intervention include sub-arachnoid hemorrhage. Geometric modeling of aneurysm being the first step in the treatment planning, the scientists therefore focus more on segmentation of aneurysm rather than its detection. A successful aneurysm detection among the bunch of vessels would certainly facilitate and ease the segmentation process. In this work, we present a novel method for aneurysm detection; the key contributions are: contrast enhancement of input image using stochastic resonance concept in wavelet domain, adaptive thresholding, and modified Hough Circle Transform. Experimental results show that the proposed method is efficient in detecting the location and type of aneurysm. This work was partly supported by NPRP Grant #NPRP 5-792-2-328 from the Qatar National Research Fund (a member of the Qatar Foundation). Scopus

Cerebral aneurysm is a life-threatening disease that may enlarge and bleed into surrounding area. Therefore, diagnosis of aneurysm is highly important as early as possible to help doctors to decide the right treatment. The HemeLB is a lattice-Boltzmann simulation framework that allows surgeons to view the simulation results of cerebral blood vessels. The HemeLB simulation framework was designed for high performance computer (HPC), which is not user friendly for the users who do not have computer science background. In this paper, we present a solution for designing and implementing HemeLB on a cost efficient embedded platform, in order to allow the HemeLB simulation framework to be potentially implemented in the local environment of hospital. The proposed work has been developed using a Zynq system-on-chip (SoC), which is a heterogeneous multi-processor system-on-chip (MPSoC) platform widely used in high performance embedded applications. Moreover, a comprehensive evaluation for the implementation is also reported in this paper. The results demonstrate that the proposed implementation is able to support the HemeLB framework on a low-cost MPSoC platform and achieving a maximum performance of 215,751 sites updates per second with only 2 cores. 2019 IEEE. This paper was made possible by National Priorities Research Program (NPRP) grant No. 5-792-2-328 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. We would also like to thank Dr Robin Richardson Scopus

Mushrooms produce a variety of bioactive compounds that are known to have anti-pathogenic properties with safer and effective therapeutic effects in human disease prognosis. The antibacterial activity of ethanol and methanol extracts of Pleurotus opuntiae were checked against pathogenic microorganisms viz. Pseudomonas aeruginosa ATCC 27853, Proteus mirabilis NCIM 2300, Proteus vulgaris NCIM 5266, Serratia marcescens NCIM 2078, Shigella flexeneri NCIM 5265, Moraxella sp. NCIM 2795, Staphylococcus aureus ATCC 25923 by agar well diffusion method at different concentrations of the extracts. Minimum Inhibitory Concentration (MIC) and Minimum Bactericidal Concentration (MBC) of the extracts was determined by INT (Iodonitrotetrazolium chloride) colorimetric assay. Extracts were standardized by thin layer chromatography (TLC) in different solvent systems. The Retention factors (Rf) of different compounds were calculated by high performance TLC (HPTLC) fingerprinting at UV 254, 366, and 540 nm before and after derivatization. The ethanol and methanol extracts of P. opuntiae showed bactericidal activity against all the test pathogens at MIC values of 15.6 to 52.08 mg/mL and 20.81 to 52.08 mg/mL respectively. Whereas the MBC values for ethanol and methanol extract of P. opuntiae against all pathogens were recorded as 26.03 to 62.5 mg/mL and 125 mg/mL respectively. Preliminary mycochemical screening of both the extracts revealed high contents of bioactive compounds. Amongst all the solvent systems used in TLC, the best result was given by chloroform + hexane (8:2) which eluted out 5 different compounds (spots). HPTLC results revealed spots with different Rf values for all the 24 compounds present. Thus, it can be inferred from the present investigation that the mycoconstituents could be an alternative medication regimen and could play a role in new drug discoveries against different infections. Further, the antimicrobial components of these mushrooms can be transformed to bioengineered antimicrobial coatings for surfaces, drug and other hybrid systems for public health implications in combating persistent infections.

Segmentation of brain aneurysm is of paramount importance in aneurysm treatment planning. However, the segmentation of intensity varying and low-contrast cerebral blood vessels is an extremely challenging task. In this paper, we present an approach to segmenting the brain vasculature in low contrast computed tomography angiography and magnetic resonance angiography. The main contributions are: (1) a stochastic resonance based methodology in discrete Hartley transform domain is developed to enhance the contrast of a selected angiographic image for patch placement, and (2) a multi-scale adaptive principal component analysis based method is proposed that estimates the phase map of input images. Level-set method is applied to the phase-map data in order to segment the vasculature. We have tested the algorithm on real datasets obtained from two sources, CIBC institute and Hamad Medical corporation. The average Dice coefficient (in %) is found to be $$94.1\pm 1.2$$ (value indicates the mean and standard deviation) whereas average false positive ratio, false negative ratio, and specificity are found to be 0.019, $$7.55\times 10^{-3}$$ , and 0.75, respectively. Average Hausdorff distance between segmented contour and ground truth is determined to be 2.97 mm. The qualitative and quantitative results show promising segmentation accuracy reflecting the potential of the proposed method.