Precise Contour Research Articles

Unmanned aerial vehicle image stitching based on multi‐region segmentation

AbstractUnmanned aerial vehicle (UAV) image stitching is a key technology for aerial remote sensing applications. Most existing image stitching methods based on optimal seamline searching algorithms can eliminate defects such as ghosting and distortion in stitched images, which unfortunately suffer from the problem that the seamline may cross those regions with significant geometric misalignment between different images. Therefore, a novel image stitching method based on multi‐region image segmentation is proposed. The algorithm starts by performing a multi‐scale morphological reconstruction in the overlapping regions between UAV images to obtain superpixel images with precise contours. Then, the fast density peaks clustering based on K‐nearest neighbours is applied to automatically determine the clustering centres and the number of clusters. By constructing a cost function, an energy map is generated in the overlapping regions between UAV images. Finally, the optimal seamline can be determined with a graph‐cut method. Compared to several popular image stitching algorithms in real experiments, the proposed method can essentially prevent the seamline from crossing significant ground objects to ensure the integrity of structural objects while achieving satisfactory accuracy and efficiency during the UAV image stitching process.

Dual convolution-transformer UNet (DCT-UNet) for organs at risk and clinical target volume segmentation in MRI for cervical cancer brachytherapy

Objective. MRI is the standard imaging modality for high-dose-rate brachytherapy of cervical cancer. Precise contouring of organs at risk (OARs) and high-risk clinical target volume (HR-CTV) from MRI is a crucial step for radiotherapy planning and treatment. However, conventional manual contouring has limitations in terms of accuracy as well as procedural time. To overcome these, we propose a deep learning approach to automatically segment OARs (bladder, rectum, and sigmoid colon) and HR-CTV from female pelvic MRI.Approach. In the proposed pipeline, a coarse multi-organ segmentation model first segments all structures, from which a region of interest is computed for each structure. Then, each organ is segmented using an organ-specific fine segmentation model separately trained for each organ. To account for variable sizes of HR-CTV, a size-adaptive multi-model approach was employed. For coarse and fine segmentations, we designed a dual convolution-transformer UNet (DCT-UNet) which uses dual-path encoder consisting of convolution and transformer blocks. To evaluate our model, OAR segmentations were compared to the clinical contours drawn by the attending radiation oncologist. For HR-CTV, four sets of contours (clinical + three additional sets) were obtained to produce a consensus ground truth as well as for inter/intra-observer variability analysis.Main results. DCT-UNet achieved dice similarity coefficient (mean ± SD) of 0.932 ± 0.032 (bladder), 0.786 ± 0.090 (rectum), 0.663 ± 0.180 (sigmoid colon), and 0.741 ± 0.076 (HR-CTV), outperforming other state-of-the-art models. Notably, the size-adaptive multi-model significantly improved HR-CTV segmentation compared to a single-model. Furthermore, significant inter/intra-observer variability was observed, and our model showed comparable performance to all observers. Computation time for the entire pipeline per subject was 12.59 ± 0.79 s, which is significantly shorter than the typical manual contouring time of >15 min.Significance. These experimental results demonstrate that our model has great utility in cervical cancer brachytherapy by enabling fast and accurate automatic segmentation, and has potential in improving consistency in contouring. DCT-UNet source code is available athttps://github.com/JHU-MICA/DCT-UNet.

Automated contouring of CTV and OARs in planning CT scans using novel hybrid convolution-transformer networks for prostate cancer radiotherapy

Purpose objective(s)Manual contouring of the prostate region in planning computed tomography (CT) images is a challenging task due to factors such as low contrast in soft tissues, inter- and intra-observer variability, and variations in organ size and shape. Consequently, the use of automated contouring methods can offer significant advantages. In this study, we aimed to investigate automated male pelvic multi-organ contouring in multi-center planning CT images using a hybrid convolutional neural network-vision transformer (CNN-ViT) that combines convolutional and ViT techniques.Materials/methodsWe used retrospective data from 104 localized prostate cancer patients, with delineations of the clinical target volume (CTV) and critical organs at risk (OAR) for external beam radiotherapy. We introduced a novel attention-based fusion module that merges detailed features extracted through convolution with the global features obtained through the ViT.ResultsThe average dice similarity coefficients (DSCs) achieved by VGG16-UNet-ViT for the prostate, bladder, rectum, right femoral head (RFH), and left femoral head (LFH) were 91.75%, 95.32%, 87.00%, 96.30%, and 96.34%, respectively. Experiments conducted on multi-center planning CT images indicate that combining the ViT structure with the CNN network resulted in superior performance for all organs compared to pure CNN and transformer architectures. Furthermore, the proposed method achieves more precise contours compared to state-of-the-art techniques.ConclusionResults demonstrate that integrating ViT into CNN architectures significantly improves segmentation performance. These results show promise as a reliable and efficient tool to facilitate prostate radiotherapy treatment planning.

TTFDNet: Precise Depth Estimation from Single-Frame Fringe Patterns.

This work presents TTFDNet, a transformer-based and transfer learning network for end-to-end depth estimation from single-frame fringe patterns in fringe projection profilometry. TTFDNet features a precise contour and coarse depth (PCCD) pre-processor, a global multi-dimensional fusion (GMDF) module and a progressive depth extractor (PDE). It utilizes transfer learning through fringe structure consistency evaluation (FSCE) to leverage the transformer's benefits even on a small dataset. Tested on 208 scenes, the model achieved a mean absolute error (MAE) of 0.00372 mm, outperforming Unet (0.03458 mm) models, PDE (0.01063 mm) and PCTNet (0.00518 mm). It demonstrated precise measurement capabilities with deviations of ~90 μm for a 25.4 mm radius ball and ~6 μm for a 20 mm thick metal part. Additionally, TTFDNet showed excellent generalization and robustness in dynamic reconstruction and varied imaging conditions, making it appropriate for practical applications in manufacturing, automation and computer vision.

Classical Orbital Floor Post-Traumatic Reconstruction vs. Customized Reconstruction with the Support of "In-House" 3D-Printed Models: A Retrospective Study with an Analysis of Volumetric Measurement.

Orbital floor fractures (OFFs) represent an interesting chapter in maxillofacial surgery, and one of the main challenges in orbit reconstruction is shaping and cutting the precise contour of the implants due to its complex anatomy. The aim of the retrospective study was to demonstrate, through pre- and postoperative volumetric measurements of the orbit, how the use of a preformed titanium mesh based on the stereolithographic model produced with 3D printers ("In-House" reconstruction) provides a better reconstruction volumetric compared to the intraoperatively shaped titanium mesh. The patients with OFF enrolled in this study were divided into two groups according to the inclusion criteria. In Group 1 (G1), patients surgically treated for OFF were divided into two subgroups: G1a, patients undergoing orbital floor reconstruction with an intraoperatively shaped mesh, and G1b, patients undergoing orbital floor reconstruction with a preoperative mesh shaped on a 3D-printed stereolithographic model. Group 2 (G2) consisted of patients treated for other traumatic pathologies (mandible fractures and middle face fractures not involving orbit). Pre- and postoperative orbital volumetric measurements were performed on both G1 and G2. The patients of both groups were subjected to the measurement of orbital volume using Osirix software (Pixmeo SARL, CH-1233 Bernex, Switzerland) on the new CT examination. Both descriptive (using central tendency indices such as mean and range) and regressive (using the Bravais-Pearson index, calculated using the GraphPad program) statistical analyses were performed on the recorded data. From 1 January 2017 to 31 December 2021, of the 176 patients treated for OFF at the "Magna Graecia" University Hospital of Catanzaro 10 fulfilled the study's inclusion criteria: 5 were assigned to G1a and 5 to G1b, with a total of 30 volumetric measurements. In G2, we included 10 patients, with a total of 20 volumetric measurements. From the volumetric measurements and statistical analysis carried out, it emerged that the average of the volumetric differences of the healthy orbits was ±0.6351 cm3, the standard deviation of the volumetric differences was ±0.3383, and the relationship between the treated orbit and the healthy orbit was linear; therefore, the treated orbital volumes tend to approach the healthy ones after surgical treatment. This study demonstrates that if the volume is restored within the range of the standardized mean, the diplopia is completely recovered already after surgery or after one month. For orbital volumes that do not fall within this range, functional recovery could occur within 6 months or be lacking. The restoration of the orbital volume using pre-modeled networks on the patient's anatomical model, printed internally in 3D, allows for more accurate reconstructions of the orbital floor in less time, with clinical advantages also in terms of surgical timing.

High-resolution imagery of the coral reefs of Gorgona Island, Colombian Pacific Ocean: An improved view for monitoring and research

The shape and size of a coral reef are both key factors influencing its ecology and biodiversity. These parameters play a fundamental role in the creation of specific habitats that support a rich biodiversity. They also greatly influence its resilience to climatic events and external influences. Here we present a practical example of photogrammetric reconstruction of the best developed coral reefs of the Colombian Pacific, located at Gorgona Island. Using unoccupied aerial vehicles (drones) and Geographic Information Systems (GIS), we produced high resolution orthophotomosaics and precise contour maps of four reefs and updated estimates of reef areas. We illustrate the improved accuracy of these maps in comparison with maps obtained from satellite imagery and conclude by highlighting the usefulness of drones to locate, measure, and monitor small coral reefs.

Accurate contour preservation for semantic segmentation by mitigating the impact of pseudo-boundaries

Accurately extracting the contours of ground objects has been an important research topic in the field of semantic segmentation of remote sensing imagery. However, existing efforts have primarily focused on refining the boundaries of predictive masks, with little consideration given to pseudo boundaries caused by abrupt changes in surface textures. Therefore, this paper addresses this challenge with the contour preservation network (CPNet), a novel semantic segmentation network that effectively mitigates pseudo-boundary effects and produces more precise contours. The key of CPNet is the boundary-guided feature alignment module (BGAM). This module employs supervised boundary guidance to adaptively transfer the model’s attention from salient areas to correct semantic boundaries. This adaptive attention transfer mechanism enables the model to suppress the impact of internal pseudo boundaries and refine contours. To further refine boundaries, a boundary point feature rectification module (ReBPM) is designed to rectify the classification of boundary points with neighbor features. Extensive experimental validations have demonstrated the effectiveness and flexibility of the proposed CPNet on ISPRS Potsdam and Vaihingen datasets. The results showed that our model outperforms other state-of-the-art methods in terms of boundary IoU, mean IoU, and mean F1-score, and it exhibits significantly superior contour preservation ability compared to other models, notably in the presence of pseudo-boundaries. The code is available at: https://github.com/angiecao/CPNet.

Sharp detection of oscillation packets in rich time-frequency representations of neural signals.

Brain oscillations most often occur in bursts, called oscillation packets, which span a finite extent in time and frequency. Recent studies have shown that these packets portray a much more dynamic picture of synchronization and transient communication between sites than previously thought. To understand their nature and statistical properties, techniques are needed to objectively detect oscillation packets and to quantify their temporal and frequency extent, as well as their magnitude. There are various methods to detect bursts of oscillations. The simplest ones divide the signal into band limited sub-components, quantifying the strength of the resulting components. These methods cannot by themselves cope with broadband transients that look like genuine oscillations when restricted to a narrow band. The most successful detection methods rely on time-frequency representations, which can readily show broadband transients and harmonics. However, the performance of such methods is conditioned by the ability of the representation to localize packets simultaneously in time and frequency, and by the capabilities of packet detection techniques, whose current state of the art is limited to extraction of bounding boxes. Here, we focus on the second problem, introducing two detection methods that use concepts derived from clustering and topographic prominence. These methods are able to delineate the packets' precise contour in the time-frequency plane. We validate the new approaches using both synthetic and real data recorded in humans and animals and rely on a super-resolution time-frequency representation, namely the superlets, as input to the detection algorithms. In addition, we define robust tests for benchmarking and compare the new methods to previous techniques. Results indicate that the two methods we introduce shine in low signal-to-noise ratio conditions, where they only miss a fraction of packets undetected by previous methods. Finally, algorithms that delineate precisely the border of spectral features and their subcomponents offer far more valuable information than simple rectangular bounding boxes (time and frequency span) and can provide a solid foundation to investigate neural oscillations' dynamics.

“The kidnappers are the police”: Criminal governance and forced migration in Central America

For decades, citizens of Northern Central America have fled their homes and their countries to escape violence and insecurity. The pervasive fear driving such displacement is rooted in profound suspicions that the state itself may be a primary accomplice in criminal schemes. While scholars and Central American publics alike agree that such “criminal governance” is very real, no one can nail down its precise contours, much less identify how deeply embedded in the state any particular illicit network may be. This article homes in on the play of perception and il/licit power to map how collective doubt about where the state ends and its underworld begins structures criminal predation and forced migration. Drawing on more than a decade of ethnographic research in Guatemala, I map victims' struggles to distinguish between their assailants’ “real” and “imagined” powers, showing how illicit networks strategically colonize and leverage this ample space of doubt. Ultimately, I argue that widespread but murky state-criminal fusion and territorial control can make a powerful feedback loop with the threat and reality of violence at criminal hands. This feedback loop in turn acts as a form of disciplinary power, molding targeted subjects and conditioning their agency in everyday life, even haunting them through time and space long after they have “escaped.” Such dynamics are a provocation for scholars to rethink misleading distinctions between the state and illicit networks in places like Guatemala to comprehend the lived consequences of contemporary criminal governance.

Investigation on the characterization of hot extruded AA7075 based metal matrix composites developed by powder metallurgy

Higher compressive stress and greater density after extrusion contribute to stronger bonds, which in turn improves mechanical and tribological properties were analyzed. In order to minimize manufacturing defects, a cosine-profiled die with mathematically precise contours was used in the thermo mechanical process. It was requested that more mechanical characterization tests, such as a compression testing and a three-point bending test, be directed to better define the material’s density, hardness, and ductility. Before and after extrusion, the prepared AMCs were put through pin-on-disc (POD) wear testing, during which the RPM of the counter disc, load (N) and track diameter (mm) were varied to simulate different two-body dry sliding wear behaviors. Hot extrusion of AA7075 aluminium matrix composites (AMCs) was investigated for its effect on the materials’ mechanical and tribological properties. These AMCs were manufactured by controlled atmospheric sintering, powder metallurgy and double axial cold compaction. The finely dispersed graphite (Gr) particles shear off at the tribo-surface, creating a solid lubricant that slows the rate of wear. The wear mechanism was found to be more complex when the loading and sliding velocities were increased.

Changes in the microscopic organisation of rat adrenal glands under the influence of Vipera berus berus venom

Snakebite envenoming is a common but neglected public health problem worldwide. Annual mortality as a result of snakebites exceeds 138,000. The organs of the endocrine system are among the first to react to the effects of snake and viper toxins. Under these conditions, the adrenal glands are involved in the pathological process and contribute to the formation of the adaptation syndrome, undergoing complex structural changes. The research aims to study the changes in the microscopic organization of the adrenal glands of rats under the influence of Vipera berus berus venom. Experimental studies were carried out on white non-linear male rats. The animals were conditionally divided into a control and an experimental group of 10 individuals. Experimental rats were injected intraperitoneally in a saline solution with a semi-lethal dose (LD50) (1.576 mg/g-1) of Vipera berus berus venom. Animals of the control group were injected intraperitoneally with only saline solution. Rats were removed from the experiment 24 hours after exposure to the venom and anaesthetized by decapitation. Adrenal gland samples were taken for microscopic examination. Fixation of the material and preparation of paraffin blocks were carried out according to generally accepted methods. Histological preparations of the adrenal glands were stained with hematoxylin and eosin. Histological preparations were studied using an SEO SСAN light microscope. Under the influence of Vipera berus berus venom in zona glomerulosa of the adrenal cortex, moderately pronounced pathological changes were found, including vacuolization and granularity of the cytoplasm of endocrinocytes, loss of precise contours of nuclei, their hyperchromasia, expansion of lumens of sinusoidal capillaries, accumulation of erythrocytes in them. Under these conditions, zona fasciculata is characterized by significant cell granularity and perinuclear edema. Less pronounced structural organization changes were noted in the zona reticularis of the adrenal cortex. Endocrinocytes of this zone had small sizes, eosinophilic cytoplasm and dark nuclei. In the medulla of the adrenal glands, the cells were large in size and had indistinct contours, the cytoplasm was characterized by basophilic granularity, and the nuclei were light due to the predominance of euchromatin. The most pronounced effect of Vipera berus berus venom was on the zona glomerulosa and zona fasciculata of the adrenal cortex; most of the morphological signs of pathology in which were caused by a violation of protein metabolism in the cells of the parenchymal and stromal elements of this organ.

Visual Detection Method for Missing Infusion Bag Pipeline

As an essential medical device, a bag infusion set is often used for intravenous infusion, and an infusion bag is an essential part of the bag infusion set. Due to the unavoidable defects in the production process, quality detection of infusion bags is critical to ensure the use quality of the infusion set. In this paper, we adopt a machine vision system to inspect the assembly quality of the lanyard and dosing interface of liquid bag assembly and conduct in-depth discussion and research from image acquisition, a defect detection strategy, and a defect detection algorithm of a vision system for two defects of lanyard missing and dosing interface missing. The design of the image acquisition auxiliary mechanism is realized to solve the complex problem of image acquisition due to the irregular shape of the liquid bag assembly; based on determining the defect detection strategy, the algorithm study of contour extraction is finally completed through comparison experiments to extract a precise contour of the liquid bag piping area; finally, the virtual straight line method is proposed and combined with the ROI selected according to the position feature of the outer rectangle of the contour in this paper, the count of the number of contours is completed, and the defect detection goal is finally achieved. The pipeline defect detection rate of the method proposed in this paper reaches 100%, which can perfectly replace the existing manual visual inspection and reduce the employment cost of enterprises.

Rail profile matching method based on stepped calibration plate and improved SURF algorithm

In order to avoid the calibration plane and laser plane do not coincide and the rail profile is difficult to match dynamically, a stepped calibration plate is made, and a rail profile matching method based on the improved SURF algorithm and random sample consensus algorithm (RANSAC) is proposed. Firstly, a stepped rail profile calibration plate is designed and fabricated, which can directly calibrate the laser plane, avoiding the problem that the calibration plane and the line laser plane do not coincide completely in practical application. In the calibration process, collect the optimal control points and calibrate the image by polynomial transformation and bilinear interpolation, then complete contour rough matching using SURF algorithm. During the SURF algorithm, construct the matching constraint of the center of the orbit waist to delete part of the false matches. Finally, the improved RANSAC algorithm is used to filter the wrong matches, achieving precise contour matching. The experimental results show that compared with traditional contour matching methods, the improved contour matching algorithm has higher accuracy and stronger robustness.

Multilevel comparison of deep learning models for function quantification in cardiovascular magnetic resonance: On the redundancy of architectural variations.

Cardiac function quantification in cardiovascular magnetic resonance requires precise contouring of the heart chambers. This time-consuming task is increasingly being addressed by a plethora of ever more complex deep learning methods. However, only a small fraction of these have made their way from academia into clinical practice. In the quality assessment and control of medical artificial intelligence, the opaque reasoning and associated distinctive errors of neural networks meet an extraordinarily low tolerance for failure. The aim of this study is a multilevel analysis and comparison of the performance of three popular convolutional neural network (CNN) models for cardiac function quantification. U-Net, FCN, and MultiResUNet were trained for the segmentation of the left and right ventricles on short-axis cine images of 119 patients from clinical routine. The training pipeline and hyperparameters were kept constant to isolate the influence of network architecture. CNN performance was evaluated against expert segmentations for 29 test cases on contour level and in terms of quantitative clinical parameters. Multilevel analysis included breakdown of results by slice position, as well as visualization of segmentation deviations and linkage of volume differences to segmentation metrics via correlation plots for qualitative analysis. All models showed strong correlation to the expert with respect to quantitative clinical parameters (rz ' = 0.978, 0.977, 0.978 for U-Net, FCN, MultiResUNet respectively). The MultiResUNet significantly underestimated ventricular volumes and left ventricular myocardial mass. Segmentation difficulties and failures clustered in basal and apical slices for all CNNs, with the largest volume differences in the basal slices (mean absolute error per slice: 4.2 ± 4.5 ml for basal, 0.9 ± 1.3 ml for midventricular, 0.9 ± 0.9 ml for apical slices). Results for the right ventricle had higher variance and more outliers compared to the left ventricle. Intraclass correlation for clinical parameters was excellent (≥0.91) among the CNNs. Modifications to CNN architecture were not critical to the quality of error for our dataset. Despite good overall agreement with the expert, errors accumulated in basal and apical slices for all models.

Abstract 2767: Simultaneous 18F-FDG PET/MR imaging for metastases identification in a disseminated human preclinical melanoma model

Abstract Melanoma is a skin cancer at high risk of metastatic progression, hence associated with poorer prognosis. Imaging represents an essential tool to assess tumor stage, but also to monitor disease progression and therapy response. However, a single imaging technique cannot efficiently detect and characterize all metastases. Therefore, we evaluated the potential of simultaneous 18F-FDG PET/MR imaging in preclinical models of disseminated melanoma at different stages. Female Nude and SDRG rats were intravenously injected with CMEL 5 cells, originating from brain metastases of a human melanoma. On day (D) 60, 70, and 80 post-injection, Nude rats were imaged using brain T2-weighted (T2w) 7T MRI and whole-body simultaneous 18F-FDG (10-15 MBq) PET/MR. Furthermore, whole-body and brain T2w MR images were acquired using a 4.7T MRI on D20 and D34 post-injection in SDRG rats. Visual detection and quantitative analysis of lesions were performed on MRI or PET/MR images, and results were confronted with necropsy (melanin pigmented lesions) and gamma counting data. From D60 post-CMEL-5 cell injection, all Nude animals exhibited metastases in one to four different locations. Moreover, most of metastases locations found in clinical setting were detected in our experimental model using simultaneous 18F-FDG PET/MR imaging, even allowing the observation of brain lesions on T2w MR images. MRI further allowed precise contouring of each organ of interest to effectively identify bone, lung, adrenal and spleen metastases with enhanced 18F-FDG uptake. Additionally, PET image quantification in adrenals was consistent with gamma counting results. As anticipated, small lesions below PET spatial resolution limit (i.e. <1 mm) were not detected by imaging. SDRG rats displayed a faster and more homogenous tumor spread in brain, lungs and liver on MR images, while more metastases were seen during autopsy (additional spreading to pancreas, adrenals, reproductive system, bones, lymph nodes and skin). The experimental model of disseminated CMEL-5 melanoma in Nude rats demonstrates its ability to mimic the human pathology. Hence, it appears to be a promising tool to monitor therapy response and potentially recognize progression from pseudo-progression. Simultaneous PET/MR imaging has shown to be more effective than stand-alone techniques in detecting melanoma dissemination, except for brain and small lesions for which the PET data are altered by partial volume effect. Thus, by complementing previously acquired MR images from the novel SDRG rat model with simultaneously acquired PET images, we could also provide insights on this promising metastatic model. Citation Format: Alexandra Oudot, Peggy Provent, Alan Courteau, Nicolas Hoffmann, Melanie Guillemin, Jordan Longin, Sarah Belderbos, Bertrand Collin, Marc Hillairet de Boisferon, Alexandre Cochet, Cyril Berthet. Simultaneous 18F-FDG PET/MR imaging for metastases identification in a disseminated human preclinical melanoma model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2767.

Precise Identification and Quantitative Analysis of Right Ventricle Using Hierarchical Intensity Clustering with Markov Random Field for Cardiac Mr Images

Precise localization and quantitative analysis of the right ventricle from cardiac magnetic resonance imaging (CMRI) images is imperative for assessing cardiopulmonary and cardiovascular mal-functionalities. Due to its poorly defined borders, precise contouring of the RV in CMRI images continues to be difficult. An approach for contouring the right ventricle based on a hierarchical intensity-based clustering method followed by Markov random field is proposed in this paper to overcome this difficulty.Because our method offers localization for each time step, it enables comprehensive right ventricle analysis during the whole cardiac-cycle. It also allows automatic prediction systems of volumetric parameters such as end systole and end diastolestages.48 human participants' cardiac MRI scans were used to validate the method. The presented scheme yielded significantly less variance than (approximately one half) compared to the reference standard of manually determined RV contours by clinical experts. This approach obtained mean Dice-Coefficient and Haussdorff-Distance of 0.92 and 5.25 mm 0.94 and 5.68 mm on the validation and tests. Further the results are evaluated using diagnosis metrics such as end diastole volume, end systole volume, and ejection fraction. Our model heading towards accurate RV localization at endocardium borders in cardiac MRI.

Keeping It Complex With Philip Hunton, John Locke, and the United States Federal Judiciary: On the Merit of Murkiness in Separation of Powers Jurisprudence

Abstract This article draws on the resources of a little-known political theorist, Philip Hunton, to explain the function of “murky” jurisprudence in the maintenance of separation of powers over time. In the era immediately before the drafting of the United States Constitution, separation of powers was a touted remedy to tyranny. But if government is thus moderated, a critical question arises: who will judge the precise contours of each institution's powers? This article addresses this longstanding question by comparing the solutions offered by Philip Hunton, John Locke, and the United States judiciary. I conclude that the judiciary's decried inability to clarify the limits of its own power is justified by Hunton's obscure explanation that separation of powers can only function so long as murkiness shrouds questions of ultimate institutional authority.

Enhancement of grain refinement and heat resistance in TiB2-reinforced Al-Cu-Mg-Fe-Ni matrix composite additive manufactured by electron beam melting

Additive manufacturing (AM) by electron beam melting (EBM) has many advantages over the laser-based AM techniques, but is subjected to a lower cooling rate and a long time thermal exposure with a much higher temperature. Therefore, the EBMed aluminum alloys commonly have coarser solidification structures such that lower mechanical properties compared to their SLMed counterparts. In this work, we proposed a strategy of introducing TiB2 particles into heat-resistant Al-Cu-Mg-Fe-Ni alloy to enhance the grain refinement and heat resistance for EBM fabrication. Applying a “presintering-densification” scanning strategy, near fully dense samples were build up with precise contours and good surface quality. The as-built EBMed TiB2/Al-Cu-Mg-Fe-Ni composite possessed a highly homogeneous microstructure with uniformly distributed TiB2 and Fe/Ni rich intermetallic particles. A fully equiaxed grain structure was achieved in the composite with a profoundly refined grain size of ~9 µm. The improved grain refinement effect was attributed to the sufficient activation of TiB2 particles as heterogeneous nuclei under a high cooling rate. Additionally, the solidified microstructure has shown an excellent thermal stability under ~500 °C during EBM process. The coexistence of thermal stable TiB2 and Fe, Ni-rich intermetallics at grain boundaries restricted the grain growth effectively. The as-built composite exhibited a high tensile strength of 253.4 MPa with an outstanding elongation of 13.5 %. After a T6-like heat treatment, the tensile strength was increased to 322 MPa with an elongation of 9.8 %. This study might shed a new light on designing high performance aluminum alloys/composites suitable for EBM technique.

Copyfraud and Other Abuses of Intellectual Property Law

Copyright gives creators a monopoly on most uses of their work throughout their lives and for seventy years post mortem. Copyfraud, in Mazzone's striking but far from unjustified usage, is a claim of ownership made by institutions and individuals that do not possess it. To discover how prevalent such frauds are (and the degree to which they constrain and contort writers, musicians, filmmakers, and others) is truly amazing. Mazzone deals only with the US, but though the precise contours of copyright are slightly different in the UK and the EU, the same kinds of overreach arise more or less everywhere nowadays. Who are the perpetrators? Museums that claim copyright in images of works that they own or hold but that are in the public domain; picture libraries that charge license fees for out-of-copyright works; publishers who make false assertions of copyright in government publications, even in law school editions of the US Constitution; media corporations that claim to be able to license excerpts of works that they own despite the provisions of the (admittedly vague) “fair use” clause in long-established copyright laws; and the heirs and estates of writers who claim control of all uses made of the published and unpublished words of their fathers, aunts, and so forth. Publishers collude in this now rampant abuse of public access to cultural goods by insisting that authors of fiction and nonfiction works undertake to clear copyright to material borrowed from sources, a laborious process for which most writers are ill equipped. The result is that key evidence in scholarly work is often simply left out. In documentary film, the effects of copyfraud can be seen only in those films that are not made: the industry convention—“when in doubt, obtain a license”—makes many subjects financially and practically unviable.This book should have been a wake-up call, when it appeared a decade ago, for all involved in what is now called “cultural production” to reassert their rights, and the rights of the public, to free access to material that is not and never will be privately owned. Alas, Mazzone's detailed chapters on what ought to be done next have remained dead letters. It is time to reread them and to heave the ball back down the false mountain of improper claims of ownership made by corporations and individuals who know what they are doing and know they can get away with it. This laudably clear, direct, and readable book brings to mind an old English rhyme that remains as relevant today as it ever was: The law doth punish man or woman / That steals the goose from off the common / But lets the greater felon loose / Who steals the common from the goose.

Weakly Supervised Polyp Segmentation in Colonoscopy Images Using Deep Neural Networks.

Colorectal cancer (CRC) is a leading cause of mortality worldwide, and preventive screening modalities such as colonoscopy have been shown to noticeably decrease CRC incidence and mortality. Improving colonoscopy quality remains a challenging task due to limiting factors including the training levels of colonoscopists and the variability in polyp sizes, morphologies, and locations. Deep learning methods have led to state-of-the-art systems for the identification of polyps in colonoscopy videos. In this study, we show that deep learning can also be applied to the segmentation of polyps in real time, and the underlying models can be trained using mostly weakly labeled data, in the form of bounding box annotations that do not contain precise contour information. A novel dataset, Polyp-Box-Seg of 4070 colonoscopy images with polyps from over 2000 patients, is collected, and a subset of 1300 images is manually annotated with segmentation masks. A series of models is trained to evaluate various strategies that utilize bounding box annotations for segmentation tasks. A model trained on the 1300 polyp images with segmentation masks achieves a dice coefficient of 81.52%, which improves significantly to 85.53% when using a weakly supervised strategy leveraging bounding box images. The Polyp-Box-Seg dataset, together with a real-time video demonstration of the segmentation system, are publicly available.