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(1) Background: The paper aims to review the available evidence regarding the health risk of the aerosolization induced by laparoscopy induced and impact of the COVID-19 pandemic upon minimally invasive surgery. (2) Materials and methods: A systematic review of the literature was performed on PubMed, Medline and Scopus until 10 July. (3) Results: Chemicals, carcinogens and biologically active materials, such as bacteria and viruses, have been isolated in surgical smoke. However, the only evidence of viral transmission through surgical smoke to medical staff is post-laser ablation of HPV-positive genital warts. The reports of SARS-CoV-2 infected patients who underwent laparoscopic surgery revealed the presence of the virus, when tested, in digestive wall and stools in 50% of cases but not in bile or peritoneal fluid. All surgeries did not result in contamination of the personnel, when protective measures were applied, including personal protective equipment (PPE) and filtration of the pneumoperitoneum. There are no comparative studies between classical and laparoscopic surgery. (4) Conclusions: Previously published data showed there is a possible infectious and toxic risk related to surgical smoke but not particularly proven for SARS-CoV-2. Implementing standardized filtration systems for smoke evacuation during laparoscopy, although increases costs, is necessary to increase the safety and it will probably remain a routine also in the future.

This paper investigates the feasibility of person identification based on feature points extracted from palmprint images. Our approach first extracts a set of feature points along the prominent palm lines (and the associated line orientation) from a given palmprint image. Next we decide if two palmprints belong to the same hand by computing a matching score between the corresponding sets of feature points of the two palmprints. The two sets of feature points/ orientations are matched using our previously developed point matching technique which takes into account the non-linear deformations as well as the outlier points present in the two sets. The estimates of the matching score distributions for the genuine and imposter sets of palm pairs showed that palmprints have a good discrimination power. The overlap between the genuine and imposter distributions was found to be about 5%. Our preliminary results indicate that adding palmprint information may improve the identity verification provided by fingerprints in cases where fingerprint images cannot be properly acquired (e.g., due to dry skin).

A new fully automated shape learning method is presented. It is based on clustering a set of training shapes in the original shape space (defined by the coordinates of the contour points) and performing a Procrustes analysis on each cluster to obtain cluster prototypes and information about shape variation. The main difference from previously reported methods is that the training set is first automatically clustered and those shapes considered to be outliers are discarded. The second difference is in the manner in which registered sets of points are extracted from each shape contour. As a direct application of our shape learning method, an 11-structure shape model of brain substructures was extracted from MR image data, an eigen-shape model was automatically trained, and employed to segment several MR brain images not present in the shape-training set. A quantitative analysis of our shape registration approach, within the main cluster of each structure, shows that our results compare very well to those achieved by manual registration; achieving an average rms error of about 1 pixel. Our approach can serve as a fully automated substitute to the tedious and time-consuming manual shape registration and analysis.

Magnetic resonance imaging (MRI) of the brain, followed by automated segmentation of the corpus callosum (CC) in midsagittal sections has important applications in neurology and neurocognitive research since the size and shape of the CC are shown to be correlated to sex, age, neurodegenerative diseases and various lateralized behavior in man. Moreover, whole head, multispectral 3D MRI recordings enable voxel-based tissue classification and estimation of total brain volumes, in addition to CC morphometric parameters. We propose a new algorithm that uses both multispectral MRI measurements (intensity values) and prior information about shape (CC template) to segment CC in midsagittal slices with very little user interaction. The algorithm has been successfully tested on a sample of 10 subjects scanned with multispectral 3D MRI, collected for a study of dyslexia. We conclude that the proposed method for CC segmentation is promising for clinical use when multispectral MR images are recorded.

We present a method for personal authentication based on deformable matching of hand shapes. Authentication systems are already employed in domains that require some sort of user verification. Unlike previous methods on hand shape based verification, our method aligns the hand shapes before extracting a feature set. We also base the verification decision on the shape distance which is automatically computed during the alignment stage. The shape distance proves to be a more reliable classification criterion than the handcrafted feature sets used by previous systems. Our verification system attained a high level of accuracy: 96.5% genuine accept rate vs. false accept rate. This performance is further improved by learning an enrolment template shape for each user.

This paper describes a segmentation technique to automatically extract the myocardium in 4D cardiac MR images for quantitative cardiac analysis and the diagnosis of patients. Three different modules are presented. The automatic localization algorithm is able to approximately locate the left ventricle in an image using a maximum discrimination technique. Then, the local deformation algorithm can deform active contours so that they align to the edges in the image to produce the desired outlining of the myocardium. Finally, the global localization algorithm is able to propagate segmented contours from one image in the data set to all the others. We have experimented with the proposed method on a large number of patients and present some examples to show the strengths and pitfalls of our algorithm.

The goal of this study is to detect the main road network in high resolution, panchromatic SPOT satellite images. We describe an automatic procedure for road detection which has the following advantages over the previous approaches: 1) it does not require manual initialization; 2) it is able to detect some of the secondary roads in addition to the main highways; and 3) the detection time is small (/spl sim/3 min) even on large images.

Background: Based on previous studies and due to the characteristics of dyslexia as an auditory phonological decoding disorder, we predicted that the shape of the posterior corpus callosum (CC) would differ between dyslexic and control subjects. Method: Twenty right-handed boys with developmental dyslexia were selected from a carefully screened general population sample (mean age 11 years) and compared to a matched control group. The CC contour was manually traced on the aligned midsagittal MR slice and total callosal area and its subregions were compared between the groups. A statistical shape analysis and subsequent CC classification was performed using a recently developed shape model method. Results: The shape analysis revealed shorter CC shape in the dyslexic group, localised in the posterior midbody/isthmus region. This region contains interhemispheric fibers from primary and secondary auditory cortices. A shape length difference larger than a fixed threshold in the posterior midbody region could correctly discriminate between control and dyslexic subject in 78% of the cases, where a dyslexic CC was shorter in this region than a control CC. However, there were no significant group differences with respect to overall CC area or subregions. Conclusion: A clear shape difference in the posterior midbody of the CC was found between dyslexic and control subjects. This fits with recent other studies that have reported a strong growth factor in this CC region during the late childhood years, coinciding with literacy acquisition. Our results show that the dyslexic group has not undergone the same growth pattern as the normal reading group.

A new fully automated shape learning method is presented. It is based on clustering a shape training set in the original shape space and performing a Procrustes analysis on each cluster to obtain a cluster prototype and information about shape variation. As a direct application of our shape learning method, a 17-structure shape model of brain substructures was computed from MR image data, an eigen-shape model was automatically derived. Our approach can serve as an automated substitute to the tedious and time-consuming manual shape analysis.