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Background The laparoscopic approach to liver resection may reduce morbidity and hospital stay. However, uptake has been slow due to concerns about patient safety and oncological radicality. Image guidance systems may improve patient safety by enabling 3D visualisation of critical intra- and extrahepatic structures. Current systems suffer from non-intuitive visualisation and a complicated setup process. A novel image guidance system (SmartLiver), offering augmented reality visualisation and semi-automatic registration has been developed to address these issues. A clinical feasibility study evaluated the performance and usability of SmartLiver with either manual or semi-automatic registration. Methods Intraoperative image guidance data were recorded and analysed in patients undergoing laparoscopic liver resection or cancer staging. Stereoscopic surface reconstruction and iterative closest point matching facilitated semi-automatic registration. The primary endpoint was defined as successful registration as determined by the operating surgeon. Secondary endpoints were system usability as assessed by a surgeon questionnaire and comparison of manual vs. semi-automatic registration accuracy. Since SmartLiver is still in development no attempt was made to evaluate its impact on perioperative outcomes. Results The primary endpoint was achieved in 16 out of 18 patients. Initially semi-automatic registration failed because the IGS could not distinguish the liver surface from surrounding structures. Implementation of a deep learning algorithm enabled the IGS to overcome this issue and facilitate semi-automatic registration. Mean registration accuracy was 10.9 ± 4.2 mm (manual) vs. 13.9 ± 4.4 mm (semi-automatic) (Mean difference − 3 mm; p = 0.158). Surgeon feedback was positive about IGS handling and improved intraoperative orientation but also highlighted the need for a simpler setup process and better integration with laparoscopic ultrasound. Conclusion The technical feasibility of using SmartLiver intraoperatively has been demonstrated. With further improvements semi-automatic registration may enhance user friendliness and workflow of SmartLiver. Manual and semi-automatic registration accuracy were comparable but evaluation on a larger patient cohort is required to confirm these findings.

The capabilities of mobile devices, like smartphones and tablets, are increasing every year. As each system-on-a-chip (SoC) generation provides better performance while also being more energy efficient compared to its predecessors, running computationally intensive tasks on the device becomes feasible. This enables advanced image filtering, video processing and machine learning applications based on Deep Learning. The dominant platform for mobile devices is Android, running on a hugely diverse set of devices, from low-end feature phone to high-end setop box. OpenCL allows one to harness that compute power with its portable cross-platform API and language. However, while being source portable, the achievable performance is not. The different characteristics of SoCs mean that the application developer needs to take advantage of SoC-specific capabilities to achieve maximum performance. This paper presents an analysis of OpenCL adoption across Android devices that have the Twitter Android app installed. The analysis shows that most of the sampled Android devices support OpenCL but there are differences in terms of OpenCL version support, different architectures and memory models. This analysis enables software developers to make an informed decision about which OpenCL implementations to target in order to provide performance portability across different hardware manufacturers and support the largest amount of devices possible.

Convolutional neural networks have enabled accurate image super-resolution in real-time. However, recent attempts to benefit from temporal correlations in video super-resolution have been limited to naive or inefficient architectures. In this paper, we introduce spatio-temporal sub-pixel convolution networks that effectively exploit temporal redundancies and improve reconstruction accuracy while maintaining real-time speed. Specifically, we discuss the use of early fusion, slow fusion and 3D convolutions for the joint processing of multiple consecutive video frames. We also propose a novel joint motion compensation and video super-resolution algorithm that is orders of magnitude more efficient than competing methods, relying on a fast multi-resolution spatial transformer module that is end-to-end trainable. These contributions provide both higher accuracy and temporally more consistent videos, which we confirm qualitatively and quantitatively. Relative to single-frame models, spatio-temporal networks can either reduce the computational cost by 30% whilst maintaining the same quality or provide a 0.2dB gain for a similar computational cost. Results on publicly available datasets demonstrate that the proposed algorithms surpass current state-of-the-art performance in both accuracy and efficiency., Comment: Changes: * Uploaded Vid4 results (footnote 1). * Added references [14, 29] as spatial-transformer prior art. * Fixed typos

Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large upscaling factors? The behavior of optimization-based super-resolution methods is principally driven by the choice of the objective function. Recent work has largely focused on minimizing the mean squared reconstruction error. The resulting estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details and are perceptually unsatisfying in the sense that they fail to match the fidelity expected at the higher resolution. In this paper, we present SRGAN, a generative adversarial network (GAN) for image super-resolution (SR). To our knowledge, it is the first framework capable of inferring photo-realistic natural images for 4x upscaling factors. To achieve this, we propose a perceptual loss function which consists of an adversarial loss and a content loss. The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images. In addition, we use a content loss motivated by perceptual similarity instead of similarity in pixel space. Our deep residual network is able to recover photo-realistic textures from heavily downsampled images on public benchmarks. An extensive mean-opinion-score (MOS) test shows hugely significant gains in perceptual quality using SRGAN. The MOS scores obtained with SRGAN are closer to those of the original high-resolution images than to those obtained with any state-of-the-art method., 19 pages, 15 figures, 2 tables, accepted for oral presentation at CVPR, main paper + some supplementary material

Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based methods., CVPR 2016 paper with updated affiliations and supplemental material, fixed typo in equation 4

The insufflation of the abdomen in laparoscopic liver surgery leads to significant deformation of the liver. The estimation of the shape and position of the liver after insufflation has many important applications, such as providing surface-based registration algorithms used in image guidance with an initial guess and realistic patient-specific surgical simulation.

Introduction Compared to open surgery, laparoscopic liver resection (LLR) of cancer benefits patients by reducing pain, length of stay and morbidity. However, LLR is often more challenging than open surgery due to the difficulty of identifying and dividing major vascular and bile duct branches. Some of these challenges may be resolved by using image guidance systems (IGS) to overlay a 3D model of the liver structure onto the liver seen at laparoscopy. Current IGS technologies rely on manual landmark definition or ultrasound for co-registration (alignment of 3D model and in-vivo liver) and do not take organ movement and deformation into account. Image guidance for LLR using a cone beam CT has also been attempted. Our group has developed an IGS that automatically registers a liver model derived from pre-operative CT to the in-vivo liver surface using computer vision techniques. Laparoscope position in relation to the liver is determined by optical tracking. Results from a porcine study and its first application in a patient are presented here. Method Laparoscopic microwave ablation was used to create identifiable liver “lesions” and a CT was obtained in land race pigs under general anaesthesia (GA). One week later, laparoscopic left hepatectomy was performed under GA, using our system. A 46 year old female who presented with an indeterminate liver lesion in the junction of segment 5/6 underwent a hepatic wedge resection under image guidance. Data on system and surgical performance was collected in both studies. Results Experiments were conducted in 5 animals with a successful image overlay achieved in 3 cases. Failure of overlay was attributed to distorted liver anatomy secondary to adhesions formed around regions of ablated liver. Initial registration of the overlayed 3d model was accomplished in less than 10 min (min). The setup and calibration of equipment for the clinical case took 20 min. Initial registration required 3 min and similar to the animal study did not require repetition. Image overlay was successfully achieved and the operation carried out using an ultrasonic scalpel with a total procedure time of 190 min. Estimated blood loss was Conclusion The IGS presented here has been evaluated in both a large animal model and subsequently in a clinical scenario. The system appears to be feasible for clinical use and has benefits in regards to uninterrupted surgical workflow, lack of radiation exposure and automatic compensation for organ motion. Disclosure of interest C. Schneider Grant/ Research Support from: This publication presents independent research commissioned by the Health Innovation Challenge Fund (HICF-T4–317), a parallel funding partnership between the Wellcome Trust and the Department of Health. The views expressed in this publication are those of the authors and not necessarily those of the Wellcome Trust or the Department of Health., S. Thompson Grant/ Research Support from: This publication presents independent research commissioned by the Health Innovation Challenge Fund (HICF-T4–317), a parallel funding partnership between the Wellcome Trust and the Department of Health. The views expressed in this publication are those of the authors and not necessarily those of the Wellcome Trust or the Department of Health., J. Totz Grant/ Research Support from: This publication presents independent research commissioned by the Health Innovation Challenge Fund (HICF-T4–317), a parallel funding partnership between the Wellcome Trust and the Department of Health. The views expressed in this publication are those of the authors and not necessarily those of the Wellcome Trust or the Department of Health., Y. Song Grant/ Research Support from: This publication presents independent research commissioned by the Health Innovation Challenge Fund (HICF-T4–317), a parallel funding partnership between the Wellcome Trust and the Department of Health. The views expressed in this publication are those of the authors and not necessarily those of the Wellcome Trust or the Department of Health., S. Johnsen Grant/ Research Support from: Intelligent Imaging Programme Grant, ref: EP/H046410/1, D. Stoyanov Grant/ Research Support from: This publication presents independent research commissioned by the Health Innovation Challenge Fund (HICF-T4–317), a parallel funding partnership between the Wellcome Trust and the Department of Health. The views expressed in this publication are those of the authors and not necessarily those of the Wellcome Trust or the Department of Health., S. Ourselin Grant/ Research Support from: Intelligent Imaging Programme Grant, ref: EP/H046410/1, K. Gurusamy Grant/ Research Support from: This publication presents independent research commissioned by the Health Innovation Challenge Fund (HICF-T4–317), a parallel funding partnership between the Wellcome Trust and the Department of Health. The views expressed in this publication are those of the authors and not necessarily those of the Wellcome Trust or the Department of Health., D. Hawkes Grant/ Research Support from: This publication presents independent research commissioned by the Health Innovation Challenge Fund (HICF-T4–317), a parallel funding partnership between the Wellcome Trust and the Department of Health. The views expressed in this publication are those of the authors and not necessarily those of the Wellcome Trust or the Department of Health., M. Clarkson Grant/ Research Support from: This publication presents independent research commissioned by the Health Innovation Challenge Fund (HICF-T4–317), a parallel funding partnership between the Wellcome Trust and the Department of Health. The views expressed in this publication are those of the authors and not necessarily those of the Wellcome Trust or the Department of Health., B. Davidson Grant/ Research Support from: This publication presents independent research commissioned by the Health Innovation Challenge Fund (HICF-T4–317), a parallel funding partnership between the Wellcome Trust and the Department of Health. The views expressed in this publication are those of the authors and not necessarily those of the Wellcome Trust or the Department of Health.