Surface reconstruction for planning and navigation of liver resections

AbstractComputer-assisted systems for planning and navigation of liver resection procedures rely on the use of patient-specific 3D geometric models obtained from computed tomography. In this work, we propose the application of Poisson surface reconstruction (PSR) to obtain 3D models of the liver surface with applications to planning and navigation of liver surgery. In order to apply PSR, the introduction of an efficient transformation of the segmentation data, based on computation of gradient fields, is proposed. One of the advantages of PSR is that it requires only one control parameter, allowing the process to be fully automatic once the optimal value is estimated. Validation of our results is performed via comparison with 3D models obtained by state-of-art Marching Cubes incorporating Laplacian smoothing and decimation (MCSD). Our results show that PSR provides smooth liver models with better accuracy/complexity trade-off than those obtained by MCSD. After estimating the optimal parameter, automatic reconstruction of liver surfaces using PSR is achieved keeping similar processing time as MCSD. Models from this automatic approach show an average reduction of 79.59% of the polygons compared to the MCSD models presenting similar smoothness properties. Concerning visual quality, on one hand, and despite this reduction in polygons, clinicians perceive the quality of automatic PSR models to be the same as complex MCSD models. On the other hand, clinicians perceive a significant improvement on visual quality for automatic PSR models compared to optimal (obtained in terms of accuracy/complexity) MCSD models. The median reconstruction error using automatic PSR was as low as 1.03±0.23mm, which makes the method suitable for clinical applications. Automatic PSR is currently employed at Oslo University Hospital to obtain patient-specific liver models in selected patients undergoing laparoscopic liver resection

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Full Reference Image Quality Assessment Based on Saliency Map Analysis

International audienc

Full Reference Image Quality Assessment Based on Saliency Map Analysis

International audienceRegion saliency has not been fully considered in most previous image quality assessment models. In this article, the contribution of any region to the global quality measure of an image is weighted with variable weights computed as a function of its saliency. In salient regions, the differences between distorted and original images are emphasized as if the authors are observing the difference image with a magnifying glass. Here a mixed saliency map model based on Itti's model and face detection is proposed. Both low-level features including intensity, color, orientation, and high-level features such as face are used in the mixed model. Differences in salient regions are then given more importance and thus contribute more to the image quality score. The experiments done on the 1700 distorted images of the TID2008 database show that the performance of the image quality assessment on full subsets is enhanced

A weighted distance approach to relevance feedback

Content-based image retrieval systems use low-level features like color and texture for image representation. Given these representations as feature vectors, similarity between images is measured by computing distances in the feature space. Unfortunately, these low-level features cannot always capture the high-level concept of similarity in human perception. Relevance feedback tries to improve the performance by allowing iterative retrievals where the feedback information from the user is incorporated into the database search. We present a weighted distance approach where the weights are the ratios of standard deviations of the feature values both for the whole database and also among the images selected as relevant by the user. The feedback is used for both independent and incremental updating of the weights and these weights are used to iteratively refine the effects of different features in the database search. Retrieval performance is evaluated using average precision and progress that are computed on a database of approximately 10,000 images and an average performance improvement of 19 % is obtained after the first iteration. 1

Multi-features based visual saliency detection in Surveillance video

DOI: 10.1117/12.863281International audienceThe perception of video is different from that of image because of the motion information in video. Motion objects lead to the difference between two neighboring frames which is usually focused on. By far, most papers have contributed to image saliency but seldom to video saliency. Based on scene understanding, a new video saliency detection model with multi-features is proposed in this paper. First, background is extracted based on binary tree searching, then main features in the foreground is analyzed using a multi-scale perception model. The perception model integrates faces as a high level feature, as a supplement to other low-level features such as color, intensity and orientation. Motion saliency map is calculated using the statistic of the motion vector field. Finally, multi-feature conspicuities are merged with different weights. Compared with the gaze map from subjective experiments, the output of the multi-feature based video saliency detection model is close to gaze map