New neighborhood based rough sets

Neighborhood based rough sets are important generalizations of the classical rough sets of Pawlak, as neighborhood operators generalize equivalence classes. In this article, we introduce nine neighborhood based operators and we study the partial order relations between twenty-two different neighborhood operators obtained from one covering. Seven neighborhood operators result in new rough set approximation operators. We study how these operators are related to the other fifteen neighborhood based approximation operators in terms of partial order relations, as well as to seven non-neighborhood-based rough set approximation operators

Virtual Prostate Biopsy with Prostate-specific Membrane Antigen and Magnetic Resonance Imaging: Closer to Reality in a Subgroup of Prostate Cancer Patients?

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Deep Learning approach for Classifying Trusses and Runners of Strawberries

The use of artificial intelligence in the agricultural sector has been growing at a rapid rate to automate farming activities. Emergent farming technologies focus on mapping and classification of plants, fruits, diseases, and soil types. Although, assisted harvesting and pruning applications using deep learning algorithms are in the early development stages, there is a demand for solutions to automate such processes. This paper proposes the use of Deep Learning for the classification of trusses and runners of strawberry plants using semantic segmentation and dataset augmentation. The proposed approach is based on the use of noises (i.e. Gaussian, Speckle, Poisson and Salt-and-Pepper) to artificially augment the dataset and compensate the low number of data samples and increase the overall classification performance. The results are evaluated using mean average of precision, recall and F1 score. The proposed approach achieved 91%, 95% and 92% on precision, recall and F1 score, respectively, for truss detection using the ResNet101 with dataset augmentation utilising Salt-and-Pepper noise; and 83%, 53% and 65% on precision, recall and F1 score, respectively, for truss detection using the ResNet50 with dataset augmentation utilising Poisson noise

'A net for everyone': fully personalized and unsupervised neural networks trained with longitudinal data from a single patient

With the rise in importance of personalized medicine, we trained personalized neural networks to detect tumor progression in longitudinal datasets. The model was evaluated on two datasets with a total of 64 scans from 32 patients diagnosed with glioblastoma multiforme (GBM). Contrast-enhanced T1w sequences of brain magnetic resonance imaging (MRI) images were used in this study. For each patient, we trained their own neural network using just two images from different timepoints. Our approach uses a Wasserstein-GAN (generative adversarial network), an unsupervised network architecture, to map the differences between the two images. Using this map, the change in tumor volume can be evaluated. Due to the combination of data augmentation and the network architecture, co-registration of the two images is not needed. Furthermore, we do not rely on any additional training data, (manual) annotations or pre-training neural networks. The model received an AUC-score of 0.87 for tumor change. We also introduced a modified RANO criteria, for which an accuracy of 66% can be achieved. We show that using data from just one patient can be used to train deep neural networks to monitor tumor change

k-strip: A novel segmentation algorithm in k-space for the application of skull stripping

Objectives: Present a novel deep learning-based skull stripping algorithm for magnetic resonance imaging (MRI) that works directly in the information rich k-space. Materials and Methods: Using two datasets from different institutions with a total of 36,900 MRI slices, we trained a deep learning-based model to work directly with the complex raw k-space data. Skull stripping performed by HD-BET (Brain Extraction Tool) in the image domain were used as the ground truth. Results: Both datasets were very similar to the ground truth (DICE scores of 92\%-98\% and Hausdorff distances of under 5.5 mm). Results on slices above the eye-region reach DICE scores of up to 99\%, while the accuracy drops in regions around the eyes and below, with partially blurred output. The output of k-strip often smoothed edges at the demarcation to the skull. Binary masks are created with an appropriate threshold. Conclusion: With this proof-of-concept study, we were able to show the feasibility of working in the k-space frequency domain, preserving phase information, with consistent results. Future research should be dedicated to discovering additional ways the k-space can be used for innovative image analysis and further workflows.Comment: 11 pages, 6 figures, 2 table

Thermal decomposition of PMC for fiber recovery

This paper describes efforts by Argonne National Laboratory to develop a process to recover carbon fibers from polymer matrix composite (PMC) materials. The polymer material in the matrix maybe a thermoplastic or a thermoset. Samples of panels containing PMC fibers were obtained and used in the bench-scale testing program. The authors tested three different methods for recovering these PMC fibers: thermal treatment, chemical degradation, and cryogenic methods (thermal shock treatment). The first two methods were effective in separating the carbon fibers from the polymeric substrate; the third method was not satisfactory. Carbon fibers separated from the polymer substrate using the thermal treatment method were submitted to Oak Ridge National Laboratory for analysis and evaluation. The results indicated that the carbon fibers had been cleanly separated from the polymer matrix. Their intrinsic density was 1.8473 g/cm{sup 3} and their electrical resistivity was 0.001847 ohm-cm, compared to an intrinsic density of 1.75--1.9 gm/cm{sup 3} and an electrical resistivity of 0.0002--0.002 ohm-cm for virgin fibers produced from polyacrylonitrile (PAN). Although they were not sure that the samples they processed were originally produced from PAN, they used the PAN fibers for comparison. It was also demonstrated that the surface of the recovered fibers could be reactivated to energy levels equivalent to those of reactivated virgin fibers from PAN. A comparison of the mechanical properties of the recovered fibers (without surface treatment) with those of surface-treated virgin fibers from PAN revealed that the ultimate tensile strength and the elongation at brake values are about 1/3 the values for the virgin fibers. The modulus for the recycled fibers (31.4 million pounds per square inch [psi]) was about the same as that for the virgin PAN fibers (31.2 million psi). The reason for the lower tensile strength and elongation is not clear; the authors plan to investigate it further as part of the process improvement study that is now underway. Process economics appear very promising, and a payback of less than two years is likely

AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment:Status Quo

The aortic vessel tree is composed of the aorta and its branching arteries, and plays a key role in supplying the whole body with blood. Aortic diseases, like aneurysms or dissections, can lead to an aortic rupture, whose treatment with open surgery is highly risky. Therefore, patients commonly undergo drug treatment under constant monitoring, which requires regular inspections of the vessels through imaging. The standard imaging modality for diagnosis and monitoring is computed tomography (CT), which can provide a detailed picture of the aorta and its branching vessels if completed with a contrast agent, called CT angiography (CTA). Optimally, the whole aortic vessel tree geometry from consecutive CTAs is overlaid and compared. This allows not only detection of changes in the aorta, but also of its branches, caused by the primary pathology or newly developed. When performed manually, this reconstruction requires slice by slice contouring, which could easily take a whole day for a single aortic vessel tree, and is therefore not feasible in clinical practice. Automatic or semi-automatic vessel tree segmentation algorithms, however, can complete this task in a fraction of the manual execution time and run in parallel to the clinical routine of the clinicians. In this paper, we systematically review computing techniques for the automatic and semi-automatic segmentation of the aortic vessel tree. The review concludes with an in-depth discussion on how close these state-of-the-art approaches are to an application in clinical practice and how active this research field is, taking into account the number of publications, datasets and challenges

Materials recovery from shredder residues

Each year, about five (5) million ton of shredder residues are landfilled in the US. Similar quantities are landfilled in Europe and the Pacific Rim. Landfilling of these residues results in a cost to the existing recycling industry and also represents a loss of material resources that are otherwise recyclable. In this paper, the authors outline the resources recoverable from typical shredder residues and describe technology that they have developed to recover these resources

Progress in recycling of automobile shredder residue

At Argonne National Laboratory, we have been developing a potentially economical process to recycle automobile shredder residue (ASR). We identified three potentially marketable materials that can be recovered from ASR and developed technologies to recover and upgrade these materials. We build and tested a field-demonstration plant for recycling polyurethane foam and produced about 2000 lb of recycled foam. Several 300-lb samples were sent for evaluation and were found to be of marketable quality. We are also preparing for a large-scale test in which about 200 tons of ASR-derived fines will be used as a raw material in cement making. A major cement company has evaluated small samples of fines prepared in the laboratory and found that they meet its requirements as a substitute for iron ore or mill scale. We also produced about 50 lb of recycled acrylonitrile butadiene styrene (ABS) from obsolete automobiles and found that it has properties that could be readily upgraded to meet the specifications of the automotive industry. In this paper, we briefly discuss the process as a whole and summarize the results obtained from the field work on foam and fines recycling