Oblique Views Research Articles

Advanced Injection of Botulinum Toxin in the Nasal Muscles: A Novel Dynamic Change in Facial Expression.

Among the nasal muscles, the levator labii superior alaeque nasi (LLSAN) acts as a transitional muscle that conjugates with other nasal and perinasal muscles. Thus, when treating the nasal region with Botulinum toxin (BTX), it is important to understand local nasal muscular dynamics and how they can influence the muscular dynamics of the entire face. This is a retrospective analysis of cases treated by an injection pattern encompassing the face, including nasal muscles. Photographs were taken at rest and during motion (frontal and oblique views), before and after treatment. A total of 227 patients have been treated in the last 18months with the following results: eyebrow tail lifting, softness of crow's feet, improvement of the drooping of the tip of the nose, and shortening of the lip philtrum when smiling. We present cases illustrating the use of this approach. Treating the facial muscles globally (including the frontal, corrugators, procerus, orbicularis oculi, platysma, DAO, and nasal muscles) can improve the smile and facial expressions. This is believed to occur because the elevated portion of the upper lip muscle becomes stronger as the nasal part of the LLSAN is paralyzed. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

A new imaging classification for safer radial access visceral intervention of the liver and optimal case selection: Apreliminary report.

The aim of this study was to evaluate the use of a new classification for safer transradial access hepatic interventional radiology, based on preoperative evaluation of the location of the left subclavian artery bifurcation in the aortic arch. A total of 38 consecutive patients with hepatocellular carcinoma and 74 sessions of radial access for visceral intervention (R.A.V.I.) were reviewed. We classified the location of the left subclavian artery bifurcation in the aortic arch in three areas using an oblique view computed tomography image matched with the curve of the aortic arches according to a new criteria Three Areas Criteria For R.A.V.I. (named "TAC-F-R"), and measured the required time from initial left radial artery arteriography to celiac artery or superior mesenteric artery arteriography. The median time required for left radial artery arteriography to the celiac artery or superior mesenteric artery arteriography in each of the three areas were: area A, 0:11:10 (h, min, s); area B, 0:14:44; and area C, 0:31:51. There were significant differences between each area after Bonferroni correction (p<0.01; A vs. B, p=0.086; A vs. C, p=0.001; and B vs. C, p=0.045), with areas A and B requiring a significantly shorter time. Finally, no patients showed neurogenic disfunction within 1week after the R.A.V.I. The new classification, "TAC-F-R," for safer transradial access hepatic interventional radiology is effective for avoiding difficult cases, and selects more suitable patients with hepatocellular carcinoma for the R.A.V.I.

Enabling Low Sidelobe Secret Multicast Transmission for mmWave Communication: An Integrated Oblique Projection Approach

The protection of multicast transmission with optimized secrecy in millimeter-wave (mmWave) communication is still an open problem. In this paper, we propose a low sidelobe secret multicast transmission scheme in mmWave communication based on physical layer security techniques. By utilizing oblique projection, we jointly adopt the directional modulation (DM) and the artificial noise (AN) to achieve secret multicast transmission at low computational costs, without jeopardizing the low sidelobe transmitting pattern. Specifically, considering the constraint of multibeam with the channel knowledge of all target users, a primary transmitting weight vector of the base station (BS) is designed to achieve a low sidelobe transmitting pattern. Then, in each symbol period, the transmitting weight vector of the BS is updated by performing a linear transformation on the primary weight vector with a transformation matrix, which is obtained from the oblique projection matrices of all the target users’ channel vectors. In this way, without deteriorating the primary weight vector’s low sidelobe transmitting pattern, the obtained transmitting weight vector synthesizes the expected symbols at the target users and adds randomness to the eavesdroppers at undesired directions. Finally, the simulations demonstrate that our proposed scheme achieves outstanding communication performance for the target users at low computational costs, while keeping the high symbol error rates at the undesired directions.

National diagnostic reference levels for digital diagnostic and screening mammography in Uganda.

Screening and diagnostic mammography are associated with some risk of radiation-induced breast cancer. This study was conducted to establish the National Diagnostic Reference Levels (NDRLs) for digital diagnostic and screening mammography in Uganda to achieve breast radiation dose optimization. A cross-sectional study was conducted among female participants recruited by consecutive sampling from three selected hospitals with digital mammography in Uganda. The study variables extracted from the mammography machines were exposure factors, compressed breast thickness (CBT), and Average Glandular Dose (AGD) of two standard mammogram views. The stratified National DRL was derived by calculating the 75th percentile of the AGD across all the samples at various CBT ranges for both screening and diagnostic mammography in craniocaudal (CC) and mediolateral oblique (MLO) views. We included 300 participants with mean ages of 50.28±9.32 and 47.45±13.45 years for the screening and diagnostic mammography, respectively. There were statistically significant positive correlations between AGD and exposure factors (mAs, kVp) (all p-values<0.0001). For screening mammography, mAs demonstrated a strong positive correlation (r = 0.8369 in CC, 0.8133 in MLO), whereas kVp showed a positive correlation with relatively lower coefficients (r = 0.3700 in CC, 0.3080 in MLO). For diagnostic mammography, mAs exhibited an even stronger positive correlation (r = 0.8987 in CC, 0.8762 in MLO), and kVp maintained a positive correlation with somewhat lower coefficients (r = 0.4954 in CC, 0.3597 in MLO). In screening mammography, for CBT within the range of (7-39) mm, the NDRLs were (1.5mGy, 1.66mGy) in CC) and MLO views. For CBT in the range of (40-59) mm, the NDRLs were (1.78mGy, 1.87mGy), and for CBT in the range of (60-99) mm, the NDRLs were (2.18mGy, 2.22mGy). For diagnostic mammography, the NDRLs were established as (1.7mGy, 1.91mGy), (2.00mGy, 2.09mGy), and (2.63mGy, 2.81mGy) for CBT ranges of (7-39) mm, (40-59) mm, and (60-99) mm, respectively. The NDRLs for digital screening and diagnostic mammography in Uganda have been proposed for the first time. The NDRL values in mammography should be specific to CBT ranges and mammographic views for both diagnostic and screening mammography.

Deep learning modeling using mammography images for predicting estrogen receptor status in breast cancer.

The estrogen receptor (ER) serves as a pivotal indicator for assessing endocrine therapy efficacy and breast cancer prognosis. Invasive biopsy is a conventional approach for appraising ER expression levels, but it bears disadvantages due to tumor heterogeneity. To address the issue, a deep learning model leveraging mammography images was developed in this study for accurate evaluation of ER status in patients with breast cancer. To predict the ER status in breast cancer patients with a newly developed deep learning model leveraging mammography images. Datasets comprising preoperative mammography images, ER expression levels, and clinical data spanning from October 2016 to October 2021 were retrospectively collected from 358 patients diagnosed with invasive ductal carcinoma. Following collection, these datasets were divided into a training dataset (n = 257) and a testing dataset (n = 101). Subsequently, a deep learning prediction model, referred to as IP-SE-DResNet model, was developed utilizing two deep residual networks along with the Squeeze-and-Excitation attention mechanism. This model was tailored to forecast the ER status in breast cancer patients utilizing mammography images from both craniocaudal view and mediolateral oblique view. Performance measurements including prediction accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curves (AUCs) were employed to assess the effectiveness of the model. In the training dataset, the AUCs for the IP-SE-DResNet model utilizing mammography images from the craniocaudal view, mediolateral oblique view, and the combined images from both views, were 0.849 (95% CIs: 0.809-0.868), 0.858 (95% CIs: 0.813-0.872), and 0.895 (95% CIs: 0.866-0.913), respectively. Correspondingly, the AUCs for these three image categories in the testing dataset were 0.835 (95% CIs: 0.790-0.887), 0.746 (95% CIs: 0.793-0.889), and 0.886 (95% CIs: 0.809-0.934), respectively. A comprehensive comparison between performance measurements underscored a substantial enhancement achieved by the proposed IP-SE-DResNet model in contrast to a traditional radiomics model employing the naive Bayesian classifier. For the latter, the AUCs stood at only 0.614 (95% CIs: 0.594-0.638) in the training dataset and 0.613 (95% CIs: 0.587-0.654) in the testing dataset, both utilizing a combination of mammography images from the craniocaudal and mediolateral oblique views. The proposed IP-SE-DResNet model presents a potent and non-invasive approach for predicting ER status in breast cancer patients, potentially enhancing the efficiency and diagnostic precision of radiologists.

Adaptive beamforming for maximum SINR in the presence of correlated interferences

The conventional steering vector (SV) based beamformers such as the minimum variance distortionless response (MVDR) can suffer from severe performance degradation due to the signal cancellation in the presence of correlated interferences. A partially correlated interference can be decomposed into two components, the coherent component and the other uncorrelated with the desired signal. The SVs of the desired signal and the coherent interference components compose the composite steering vector (CSV). In this paper, a beamforming method is presented that can achieve the maximum signal-to-interference-plus-noise ratio (SINR) via the use of the CSV and the interference covariance matrix (ICM) formed by the uncorrelated interference components only. The estimates of the CSV and the ICM are extracted from the sample matrix through oblique projections. In the estimation process, neither the interference powers nor the desired signal power is estimated, though the direction estimation is carried out using the multiple signal classification (MUSIC). We make theoretical performance analysis, which shows that the CSV based method is more effective as the correlation between the desired signal and the interferences increases and as the desired signal power becomes small. Simulation results demonstrate that it not only converges very quickly to the maximum SINR but also is robust to pointing errors.

A Comparative Study of Breast Mass Detection Using YOLOv8 Deep Learning Model in Various Data Scenarios on Multi-View Digital Mammograms

Breast cancer is one of the most common types of cancer among women worldwide. It typically begins with abnormal cell growth in the breast glands or milk ducts and can spread to other tissues. Many breast cancer cases start with the presence of a mass and should be carefully examined. Masses can be monitored using X-ray-based digital mammography images, including right craniocaudal, left craniocaudal, right mediolateral oblique, and left mediolateral oblique views. In this study, automatic mass detection and localization were performed on mammography images taken from the full-field digital mammography VinDr-Mammo dataset using the YOLOv8 deep learning model. Three different scenarios were tested: raw data, data with preprocessing to crop breast regions, and data with only mass regions cropped to a 1.2x ratio. The data were divided into 80% for training and 10% each for validation and testing. The results were evaluated using performance metrics such as precision, recall, F1-score, mAP, and training graphs. At the end of the study, it is demonstrated that the YOLOv8 deep learning model provides successful results in mass detection and localization, indicating its potential use as a computer-based decision support system.

Determination of Black Sea Coastline Length with Oblique Stereographic Projection Using Affine Transformation

The Black Sea basin has rich oil and natural gas resources. As a result, determining the continental shelf in international relations and maritime law is a critical issue for countries with a shoreline on the Black Sea, which is a semi-enclosed sea. Global projections are generally used for the projection of satellites used to image the earth. The use of global projections causes increased deformation in applications in local areas. For this reason, the deformation of satellite images used as a base can be reduced by converting them to the appropriate projection. In this study, the coastline lengths of the countries neighboring the Black Sea were calculated by using remote sensing images and selecting the appropriate cartographic projection due to the aim of minimum map deformation. By using different numbers of control points to determine the six parameters of the 2D Affine transformation used for coordinate transformation, the change in the accuracy of the transformation depending on the number of control points was examined. The Black Sea coastline, which was digitized in the local coordinate system via the MODIS satellite image, was transformed into coordinates in the oblique stereographic projection system with 2D affine transformation. 11 test points were used in the affine transformation parameters calculated using different control points, and root mean square error (RMSE) of approximately 6 km on the X axis and approximately 10 km on the Y axis was achieved using 25 control points. As a result of the transformation, the coastline lengths of each country bordering the Black Sea were determined in oblique stereographic projection.

Relative error-based time-limited [formula omitted] model order reduction via oblique projection

In time-limited model order reduction, a reduced-order approximation of the original high-order model is obtained that accurately approximates the original model within the desired limited time interval. Accuracy outside that time interval is not that important. The error incurred when a reduced-order model is used as a surrogate for the original model can be quantified in absolute or relative terms to access the performance of the model reduction algorithm. The relative error is generally more meaningful than an absolute error because if the original and reduced systems’ responses are of small magnitude, the absolute error is small in magnitude as well. However, this does not necessarily mean that the reduced model is accurate. The relative error in such scenarios is useful and meaningful as it quantifies percentage error irrespective of the magnitude of the system’s response. In this paper, the necessary conditions for a local optimum of the time-limited H2 norm of the relative error system are derived. Inspired by these conditions, an oblique projection algorithm is proposed that ensures small H2-norm relative error within the desired time interval. Unlike the existing relative error-based model reduction algorithms, the proposed algorithm does not require solutions of large-scale Lyapunov and Riccati equations. The proposed algorithm is compared with time-limited balanced truncation, time-limited balanced stochastic truncation, and time-limited iterative Rational Krylov algorithm. Numerical results confirm the superiority of the proposed algorithm over these existing algorithms.

Influence of orthodontic bracket prescription on smile attractiveness in patients with different sagittal skeletal patterns: A 3-dimensional cross-sectional study

The objective of this study was to evaluate the influence of orthodontic bracket prescription on smile attractiveness. Three women were chosen according to their sagittal skeletal pattern: skeletal Class I, II, or III malocclusion. For each, 3 smiling pictures were taken in frontal, oblique, and lateral views. The maxillary arch was then scanned, and a 3-dimensional digital model was constructed on the OrthoAnalyzer software. The information of 3 orthodontic prescriptions, namely Roth, MBT, and Ricketts, was simulated after the virtual placement of orthodontic brackets on 10 maxillary teeth. The simulations were then superimposed on the smile photographs in the 3 views, creating a total of 27 images. Groups of orthodontists, dentists, and laypeople rated the attractiveness of each smile using the visual analog scale. This study included 167 evaluators: 54 orthodontists, 54 dentists, and 59 laypersons. In terms of orthodontic prescription, MBT esthetic scores were 4% lower than Roth scores (P<0.001), and Ricketts scores were 13.6% lower than Roth scores (P<0.001). In terms of skeletal pattern, Class II and III scores were 15.0% and 16.4% lower than Class I scores, respectively (P<0.001). Regarding the smiling view, oblique view scores were 3.1% lower than frontal view scores (P= 0.031), whereas lateral view scores were 8.4% lower than frontal view scores (P<0.001). In contrast to gender and age, the specialty of the evaluators significantly affected the esthetic rating of the smiles. The attractiveness of a smile is affected by the prescription of the brackets. The Roth prescription tends to obtain the most favorable esthetic scores, especially in sagittal smiling images. Profile smiles with excessively proclined incisors are considered unattractive. Different views of the same smile do not get similar esthetic scores because sagittal views are rated the most severely.

Estimating Local Diagnostic Reference Levels for Mammography in Dubai.

As the total volume of mammograms in Dubai is increasing consistently, it is crucial to focus on the process of dose optimization by determining dose reference levels for such sensitive radiographic examinations as mammography. This work aimed to determine local diagnostic reference levels (DRLs) for mammography procedures in Dubai at different ranges of breast thickness. A total of 2599 anonymized mammograms were randomly retrieved from a central dose survey database. Mammographic cases for screening women aged from 40 to 69 years were included, while cases of breast implants and breast thickness outside the range of 20-100 mm were excluded. Mean, median, and 75 percentiles were obtained for the mean glandular dose (MGD) distribution of each mammography projection for all compressed breast thickness (CBT) ranges. The local DRLs for mammography in Dubai were found to be between 0.80 mGy and 0.82 mGy for the craniocaudal (CC) projection and between 0.89 mGy and 0.971.8 mGy for the mediolateral oblique (MLO) projection. Local DRLs were proposed according to different breast thicknesses, starting from 20 to 100 mm. All groups of CBT showed a slight difference in MGD values, with higher values in MLO views rather than CC views. The local DRLs in this study were lower than some other Middle Eastern countries and lower than the standard reference levels reported by the International Atomic Energy Agency (IAEA) at 3 mGy/view.

Oblique view imaging analysis of a hypothetical earth observation satellite enabled with metamaterial-based image sensors

This study presents the image quality estimates of a hypothetical earth observation satellite (EOS) equipped with metaxel-based image sensors for the oblique viewing angle of the pitch manoeuvre scenario. Oblique-view image estimation methods are proposed and demonstrated for the optical and detector images. Owing to the small detector period (p) of the metaxel supercells ( p = 1.32 μ m ), the optical system image is optics limited. For oblique angle of θ p = 15 ∘ , 30 ∘ , 45 ∘ , 60 ∘ , image quality degradation owing to the aberration remained minor and average signal to noise ratio (SNR) for nadir view was SNR = 64 because of the local field enhancement of the metaxels. However, changes in the viewing angle reduced the image quality. Correlation coefficient between synthetic ground truth and oblique viewing cases was corr = 0.833, 0.818, 0.793 and 0.745 for the viewing angles of interest listed above.

Anatomical and Three-Dimensional Study of the Female Feline Abdominal and Pelvic Vascular System Using Dissections, Computed Tomography Angiography and Magnetic Resonance Angiography.

This study describes the anatomical characteristics of the abdominal and pelvic vascular system of two healthy mature female cats via three-dimensional contrast enhanced computed tomography angiography, non-contrast enhanced magnetic resonance angiography and three-dimensional printing. Volume-rendering computed tomography angiography images were acquired from the ventral aspect using RadiAnt, Amira and OsiriX MD Dicom three-dimensional formats, and three-dimensional printing was obtained and compared with the corresponding computed tomography angiography images. Non-contrast enhanced magnetic resonance angiography was made using the time-of-flight imaging in ventral, oblique and lateral views. In addition, three cadavers with colored latex injection were dissected to facilitate the identification of the vascular structures. Three-dimensional computed tomography angiography showed the main vascular structures, whereas with the time-of-flight blood appeared with a high signal intensity compared with associated abdominal and pelvic tissues. Three-dimensional computed tomography angiography images and time-of-flight sequences provided adequate anatomical details of the main arteries and veins that could be used for future feline anatomical and clinical vascular studies of the abdomen and pelvis.

Ultrasound-assisted lumbar puncture with a horizontal and perpendicular paramedian approach based on positioning in patients with abnormal spinal anatomy: a case report and technical description

BackgroundThe use of ultrasound has been reported to be beneficial in challenging neuraxial procedures. The angled probe is responsible for the main limitations of previous ultrasound-assisted techniques. We developed a novel technique for challenging lumbar puncture, aiming to locate the needle entry point which allowed for a horizontal and perpendicular needle trajectory and thereby addressed the drawbacks of earlier ultrasound-assisted techniques.Case presentationPatient 1 was an adult patient with severe scoliosis who underwent a series of intrathecal injections of nusinersen. The preprocedural ultrasound scan revealed a cephalad probe’s angulation (relative to the edge of the bed) in the paramedian sagittal oblique view, and then the probe was rotated 90° into a transverse plane and we noted that a rocking maneuver was required to obtain normalized views. Then the shoulders were moved forward to eliminate the need for cephalad angulation of the probe. The degree of rocking was translated to a lateral offset from the midline of the spine through an imaginary lumbar puncture’s triangle model, and a needle entry point was marked. The spinal needle was advanced through this marking-point without craniocaudal and lateromedial angulation, and first-pass success was achieved in all eight lumbar punctures. Patient 2 was an elderly patient with ankylosing spondylitis who underwent spinal anesthesia for transurethral resection of the prostate. The patient was positioned anteriorly obliquely to create a vertebral rotation that eliminated medial angulation in the paramedian approach. The procedure succeeded on the first pass.ConclusionsThis ultrasound-assisted paramedian approach with a horizontal and perpendicular needle trajectory may be a promising technique that can help circumvent challenging anatomy. Larger case series and prospective studies are warranted to define its superiority to alternative approaches of lumbar puncture for patients with difficulties.

Risk of Diabetes Mellitus in Carotid Artery Involvement Among Coronary Artery Disease Patients Including History of Infection and Antibiotics Uses

Background: Diabetes mellitus causes involvement of coronary artery diseases. Objective: The purpose of the present study was to determine the risk of diabetes mellitus in the involvement of carotid artery among coronary artery disease patients. Methodology: This case-control study was conducted in the Department of Cardiology at Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh from July 2013 to June 2015 for a period of two years. Patients who had coronary artery diseases or ischemic heart diseases and diabetes mellitus undergoing coronary angiography were selected as case of this study. In control group, age, sex match patients of IHD undergoing CAG not having DM were included. Every patient was clinically examined by detailed history and physical examination during entry into study. ECG, chest X-ray, echocardiogram, blood sugar (fasting), blood 2 hrs. after 75 gm of glucose and or breakfast were analyzed. Coronary angiography was done in all patients through femoral approach by modified Seldinger technique. Carotid arteriography was performed selective and non­selective method by Hunter head or right Judkin's catheter in standard AP, lateral and oblique views. Results: A total number of 100 patients were included in this study. Of them 50 patients were coronary artery disease with diabetes mellitus and another 50 patients were coronary artery disease without diabetes mellitus. The mean height of the DM was 163.4±8.5 cm and in NDM was 160.6±7.1 cm, mean weight of DM 63.5±7.2 kg, in NDM was 62.0±15.4 kg and mean BMI in DM was 26.24±2.36 kg/m2 and in NDM was 26.38±2.05 kg/m2. The mean total cholesterol in DM was 236.9±34.1 mg/dL and in NDM was 208.0±36.4 mg/dL, mean serum TG in DM 240.5±76.3 mg/dl and in NDM was 207.6±57.8 mg/dl and mean serum IDL in DM was 166.1±130.6 mg/dL and in NDM was 142.8±30.4 mg/dL, no significant mean difference was observed between DM and NDM in height, weight and BMI (P&gt;0.05). Concussion: In conclusion, significant mean difference is observed between DM and NDM in total cholesterol, serum triglycerides. Bangladesh Journal of Medical Microbiology, July 2023;17 (2):82-87

Applicability and robustness of an artificial intelligence-based assessment for Greulich and Pyle bone age in a German cohort.

The determination of bone age (BA) based on the hand and wrist, using the 70-year-old Greulich and Pyle (G&P) atlas, remains a widely employed practice in various institutions today. However, a more recent approach utilizing artificial intelligence (AI) enables automated BA estimation based on the G&P atlas. Nevertheless, AI-based methods encounter limitations when dealing with images that deviate from the standard hand and wrist projections. Generally, the extent to which BA, as determined by the G&P atlas, corresponds to the chronological age (CA) of a contemporary German population remains a subject of continued discourse. This study aims to address two main objectives. Firstly, it seeks to investigate whether the G&P atlas, as applied by the AI software, is still relevant for healthy children in Germany today. Secondly, the study aims to assess the performance of the AI software in handling non-strict posterior-anterior (p. a.) projections of the hand and wrist. The AI software retrospectively estimated the BA in children who had undergone radiographs of a single hand using posterior-anterior and oblique planes. The primary purpose was to rule out any osseous injuries. The prediction error of BA in relation to CA was calculated for each plane and between the two planes. A total of 1253 patients (aged 3 to 16 years, median age 10.8 years, 55.7 % male) were included in the study. The average error of BA in posterior-anterior projections compared to CA was 3.0 (± 13.7) months for boys and 1.7 (± 13.7) months for girls. Interestingly, the deviation from CA tended to be even slightly lower in oblique projections than in posterior-anterior projections. The mean error in the posterior-anterior projection plane was 2.5 (± 13.7) months, while in the oblique plane it was 1.8 (± 13.9) months (p = 0.01). The AI software for BA generally corresponds to the age of the contemporary German population under study, although there is a noticeable prediction error, particularly in younger children. Notably, the software demonstrates robust performance in oblique projections. · Bone age, as determined by artificial intelligence, aligns with the chronological age of the contemporary German cohort under study.. · As determined by artificial intelligence, bone age is remarkably robust, even when utilizing oblique X-ray projections.. · Pape J, Hirsch F, Deffaa O et al. Applicability and robustness of an artificial intelligence-based assessment for Greulich and Pyle bone age in a German cohort. Fortschr Röntgenstr 2024; 196: 600 - 606.

Comprehensive Analysis of Mammography Images Using Multi-Branch Attention Convolutional Neural Network

Breast cancer profoundly affects women’s lives; its early diagnosis and treatment increase patient survival chances. Mammography is a common screening method for breast cancer, and many methods have been proposed for automatic diagnosis. However, most of them focus on single-label classification and do not provide a comprehensive analysis concerning density, abnormality, and severity levels. We propose a method based on the multi-label classification of two-view mammography images to comprehensively diagnose a patient’s condition. It leverages the correlation between density type, lesion type, and states of lesions, which radiologists usually perform. It simultaneously classifies mammograms into the corresponding density, abnormality type, and severity level. It takes two-view mammograms (with craniocaudal and mediolateral oblique views) as input, analyzes them using ConvNeXt and the channel attention mechanism, and integrates the information from the two views. Finally, the fused information is passed to task-specific multi-branches, which learn task-specific representations and predict the relevant state. The system was trained, validated, and tested using two public domain benchmark datasets, INBreast and the Curated Breast Imaging Subset of DDSM (CBIS-DDSM), and achieved state-of-the-art results. The proposed computer-aided diagnosis (CAD) system provides a holistic observation of a patient’s condition. It gives the radiologists a comprehensive analysis of the mammograms to prepare a full report of the patient’s condition, thereby increasing the diagnostic precision.

Radiological Methods for the Imaging of Congenital Malformations of C6-T1, the First and Second Sternal Ribs and Development of a Classification System, Demonstrated in Warmblood Horses

There are conflicting data in studies on malformations of the cervicothoracic (C-T) junction (C6 to T2, including the first and second ribs), but evidence is mounting that they can be of clinical significance for horses. The objectives of this study were to establish a radiographic protocol for imaging the C-T junction in the field and to classify the radiographic variations found in 39 warmblood horses presented for clinical evaluation due to behavioral or performance issues. Malformations of the ventral lamina of C6 and transposition onto the ventral aspect of C7 were seen in 37/39 (94.9%) horses for both conditions. Rib anomalies were found among the horses with C6 and C7 malformations. A missing first rib, unilateral or bilateral, was found in 3/35 (11.4%) horses, a unilateral shortening of the rib in 17/35 (48.6%) horses, a bilateral shortening of the ribs in 12/35 (34.3%) horses, bifid ribs in 3/35 (8.6%) horses, and only 4/35 (11.4%) horses had a normal length of the first rib on both sides. There was a moderately to highly significant association between the grades of left and right malformations of C6 and C7 and first ribs as well as between C6 and C7. A large number of malformations were visualized radiographically at the C-T junction using the newly described methods of latero-lateral and oblique radiographic projections, which allows for these features to be identified in living horses.

CreatureShop: Interactive 3D Character Modeling and Texturing From a Single Color Drawing.

Creating 3D shapes from 2D drawings is an important problem with applications in content creation for computer animation and virtual reality. We introduce a new sketch-based system, CreatureShop, that enables amateurs to create high-quality textured 3D character models from 2D drawings with ease and efficiency. CreatureShop takes an input bitmap drawing of a character (such as an animal or other creature), depicted from an arbitrary descriptive pose and viewpoint, and creates a 3D shape with plausible geometric details and textures from a small number of user annotations on the 2D drawing. Our key contributions are a novel oblique view modeling method, a set of systematic approaches for producing plausible textures on the invisible or occluded parts of the 3D character (as viewed from the direction of the input drawing), and a user-friendly interactive system. We validate our system and methods by creating numerous 3D characters from various drawings, and compare our results with related works to show the advantages of our method. We perform a user study to evaluate the usability of our system, which demonstrates that our system is a practical and efficient approach to create fully-textured 3D character models for novice users.

Using automated software evaluation to improve the performance of breast radiographers in tomosynthesis screening

ObjectiveTo improve breast radiographers’ individual performance by using automated software to assess the correctness of breast positioning and compression in tomosynthesis screening.Materials and methodsIn this retrospective longitudinal analysis of prospective cohorts, six breast radiographers with varying experience in the field were asked to use automated software to improve their performance in breast compression and positioning. The software tool automatically analyzes craniocaudal (CC) and mediolateral oblique (MLO) views for their positioning quality by scoring them according to PGMI classifications (perfect, good, moderate, inadequate) and checking whether the compression pressure is within the target range. The positioning and compression data from the studies acquired before the start of the project were used as individual baselines, while the data obtained after the training were used to test whether conscious use of the software could help the radiographers improve their performance. The percentage of views rated perfect or good and the percentage of views in target compression were used as overall metrics to assess changes in performance.ResultsFollowing the use of the software, all radiographers significantly increased the percentage of images rated as perfect or good in both CCs and MLOs. Individual improvements ranged from 7 to 14% for CC and 10 to 16% for MLO views. Moreover, most radiographers exhibited improved compression performance in CCs, with improvements up to 16%.ConclusionActive use of a software tool to automatically assess the correctness of breast compression and positioning in breast cancer screening can improve the performance of radiographers.Clinical relevance statementThis study suggests that the use of a software tool for automatically evaluating correctness of breast compression and positioning in breast cancer screening can improve the performance of radiographers on these metrics, which may ultimately lead to improved screening outcomes.Key Points• Proper breast positioning and compression are critical in breast cancer screening to ensure accurate diagnosis.• Active use of the software increased the quality of craniocaudal and mediolateral oblique views acquired by all radiographers.• Improved performance of radiographers is expected to improve screening outcomes.