Region Detection Research Articles

Coordinate-Independent 3-D Ultrasound Principal Stretch and Direction Imaging.

In clinical ultrasound, current 2-D strain imaging faces challenges in quantifying three orthogonal normal strain components. This requires separate image acquisitions based on the pixel-dependent cardiac coordinate system, leading to additional computations and estimation discrepancies due to probe orientation. Most systems lack shear strain information, as displaying all components is challenging to interpret. This paper presents a 3-D high-spatial-resolution, coordinate-independent strain imaging approach based on principal stretch and axis estimation. All strain components are transformed into three principal stretches along three normal principal axes, enabling direct visualization of the primary deformation. We devised an efficient 3-D speckle tracking method with tilt filtering, incorporating randomized searching in a two-pass tracking framework and rotating the phase of the 3-D correlation function for robust filtering. The proposed speckle tracking approach significantly improves estimates of displacement gradients related to the axial displacement component. Non-axial displacement gradient estimates are enhanced using a correlation-weighted least-squares method constrained by tissue incompressibility. Simulated and in vivo canine cardiac datasets were evaluated to estimate Lagrangian strains from end-diastole to end-systole. The proposed speckle tracking method improves displacement estimation by a factor of 4.3 to 10.5 over conventional 1-pass processing. Maximum principal axis/direction imaging enables better detection of local disease regions than conventional strain imaging. Coordinate-independent tracking can identify myocardial abnormalities with high accuracy. This study offers enhanced accuracy and robustness in strain imaging compared to current methods.

Resolution enhancement of SMOS brightness temperatures: Application to melt detection on the Antarctic and Greenland ice sheets

A large part of the surface of the Greenland Ice Sheet (GrIS) and the margins of Antarctica are melting every summer, affecting their surface mass balance. Wet/dry snow status has been detected for decades using the peaks of brightness temperature at 19 GHz, and more recently at L-band (1.4 GHz) using both the SMOS and SMAP missions. SMOS owns a longer time series than SMAP with data since 2010, but the 52.5°incidence bin in the Level 3 (L3) product from Centre Aval de Traitement des Données SMOS (CATDS) that was previously used to detect melt suffers from a coarse spatial resolution. For this reason, we developed a new SMOS enhanced resolution brightness temperature (TB) product building on the radiometer version of the Scatterometter Image Reconstruction (rSIR) algorithm. We also exploited the SMOS L1C observations near 40°incidence angle instead of 52.5°as the native spatial resolution of SMOS is better at low incidence. The new product is posted on a 12.5 km polar stereographic grid and covers all the GrIS and Antarctica for 2010–2024 with twice-daily morning and afternoon acquisitions. The effective spatial resolution was evaluated to ∼30 km, a 30% enhancement compared to the SMOS L3TB at 40°and almost a 50% enhancement compared to the SMOS L3TB at 52.5°. Then, we applied a melt detection algorithm to both the enhanced resolution product at 40°and the L3TB product at 52.5°which is used in the literature. The spatial resolution enhancement results not only in the detection of smaller melt regions but also in a widespread increase in the annual number of melt days. This increase is larger than 30 days per year in the GrIS percolation area and on multiple Antarctic ice shelves. This is primarily due to the mix of dry and wet snow regions near the ice shelves grounding line, resulting in lower brightness temperature peaks in the SMOS L3TB product due to a large power spread. These findings highlight the dependence of melt detection in particular, and geophysical applications in general, on the spatial resolution of passive microwave observations. This study provides a new open dataset suitable to monitor melt at the surface and at depth on the two main ice-sheets.

Genome-wide association study (GWAS) analyses of early anatomical changes in rose adventitious root formation

Adventitious root (AR) formation is a genetically complex trait with high genotypic variability. Therefore, only a limited range of cultivars are currently propagated by cuttings in rose. In this study, we analysed the anatomy of in vitro shoots, the early formation of root primordia (RP) and the formation of ARs in a diverse set of 106 rose genotypes. Correlation analysis indicated that the growth in shoot diameter and the vasculature dimensions after 1 week of rooting contributed to successful AR formation. Using phenotypic data for genome-wide association studies (GWAS) analyses, nine significantly associated single nucleotide polymorphisms (SNPs) and genomic regions contributing to various RP and AR formation traits were identified. The contribution of genomic regions to trait variation was notably greater for traits associated with earlier processes than for traits associated with later developmental stages. The combination of RP and AR data allowed the detection of regions by GWAS that contain factors that potentially limit RP emergence. Homologues of 47 genes known to be involved in AR formation from the literature could be assigned to the identified peaks. Further studies are needed to investigate the suitability of SNPs exhibiting strong effects as allele-specific PCR markers for use in breeding.

Automated Breast Ultrasound With Remote Reading for Primary Breast Cancer Screening: A Prospective Study Involving 46 Community Health Centers in China.

Background: China has faced barriers to implementation of a population-based mammographic screening program. Breast ultrasound provides an alternative screening modality to mammography in low-resource settings. Objective: To evaluate the performance of ABUS with remote reading as the primary screening modality for breast cancer. Methods: This prospective study enrolled asymptomatic women 35-69 years old from 46 community health centers across 18 provinces representing all six regions of China from January 2021 to December 2021. Participants underwent screening ABUS as the sole breast cancer screening modality, with images acquired by a technologist at the community health center. The 3D volumetric data were transferred via cloud-based software to a single remote reading center, where examinations were interpreted independently in batches by two subspecialized breast radiologists using BI-RADS; a third radiologist at the remote reader center resolved discrepancies. Diagnostic reports were returned to the community centers, and patients sought follow-up care at local hospitals. The reference standard incorporated a combination of histopathology and 24-month follow-up. Outcomes measures included cancer detection rate, abnormal interpretation rate (AIR), sensitivity, specificity, biopsy rate, and PPV. Results: The final analysis included 5978 enrolled participants (median age, 46 years [IQR 40-52 years]) who underwent screening ABUS at the community health centers with subsequent remote reading. A total of 24 ABUS-detected cancers and two interval cancers were diagnosed. The cancer detection rate was 4.0 per 1000 women (95% CI: 2.7-5.9), and the AIR was 11.9% (95% CI: 11.1-12.7). A total of 95.8% (23/24) of ABUS-detected cancers were invasive. The 23 invasive cancers had a median diameter of 10.0 mm; 73.9% (17/23) were node-negative. Sensitivity was 92.3% (95% CI: 75.9-97.9), and specificity was 88.4% (95% CI: 87.6-89.2). The biopsy rate was 1.7% (95% CI: 1.4-2.0), and the PPV of biopsy was 24.0% (95% CI: 16.7-33.2). Conclusion: ABUS screening with remote reading met benchmark performance for cancer detection in comparison to mammography, with infrequent interval cancers. Clinical Impact: ABUS with remote reading holds promise in enhancing access to breast cancer screening and early detection in low-resource settings or underserved regions where mammographic screening is not established.

Detection, Isolation and Quantification of Myocardial Infarct with Four Different Histological Staining Techniques.

The precise quantification of myocardial infarction is crucial for evaluating therapeutic strategies. We developed a robust, color-based semi-automatic algorithm capable of infarct region detection, isolation and quantification with four different histological staining techniques, and of the isolation and quantification of diffuse fibrosis in the heart. Our method is developed based on the color difference in the infarct and non-infarct regions after histological staining. Mouse cardiac tissues stained with Masson's trichrome (MTS), hematoxylin and eosin (H&E), 2,3,5-Triphenyltetrazolium chloride and picrosirius red were included to demonstrate the performance of our method. We demonstrate that our algorithm can effectively identify and produce a clear visualization of infarct tissue in the four staining techniques. Notably, the infarct region on an H&E-stained tissue section can be clearly visualized after processing. The MATLAB-based program we developed holds promise for infarct quantification. Additionally, our program can isolate and quantify diffuse fibrotic elements from an MTS-stained cardiac section, which suggests the algorithm's potential for evaluating pathological cardiac fibrosis in diseased cardiac tissues. We demonstrate that this color-based algorithm is capable of accurately identifying, isolating and quantifying cardiac infarct regions with different staining techniques, as well as diffuse and patchy fibrosis in MTS-stained cardiac tissues.

Attenuative Unified U-Net with Conditional PatchMatch Algorithm for Edge Preserving Salient Object Detection

The rise of Deep Neural Networks (DNNs) has significantly boosted salient object detection in computer vision tasks. However, downsampling methods like striding and pooling often introduce bluish artifacts near edges, hindering detection accuracy. To address this, the "Attenuative Unified U-Net with Conditional PatchMatch Algorithm" is proposed. The method employs Gaussian smoothing for noise reduction and depth refinement in preprocessing. In existing methods, the Edge-preserving salient object detection struggles with cluttered backgrounds and connected objects, like a bird on a rock, making it difficult to accurately distinguish edges and salient regions due to shared boundaries. Hence, an Attenuative Unified Backpropagated Entropy UNet is proposed, which integrates an Attention Mechanism that enhance feature map spatial weight assignment, emphasizing features or regions that are judged most relevant for edge and salient region detection. Then, the Cascaded Laplace pyramid is incorporated into the Unified U-Net’s design for multi-scale information processing, capturing contextual details effectively. The Backpropagated Entropy Loss Function is then created to ensure accurate and confident fused image output. The depth map, edge map, and salient area map are combined in the U-Net’s final output layer to create an overall fused image. Next, to addresses depth discontinuity problem a noteworthy novel idea the Conditional PatchMatch Random Field Algorithm (CPMRF), which combines PatchMatch efficiency and CRFs’ contextual modeling. PatchMatch iteratively propagates matches from neighboring patches to find similar patches in the remaining portion of the image and Conditional Random Field (CRF) is the next step, which improves the PatchMatch results by taking into account the connections between the matched patches and their neighboring areas. Hence, the experimental outcomes of the proposed model effectively show the improved detection of edge in salient object detection with better accuracy, precision, recall, MaxF, and F1 score with minimized MAE.

Vispro improves imaging analysis for Visium spatial transcriptomics.

Spatial transcriptomics (ST) is a cutting-edge technology that enables the comprehensive analysis of gene expression while preserving the spatial context of tissues. Within this framework, histological images serve a crucial role, providing spatially cohesive information that is often challenging to capture through gene expression alone. However, the imaging process in ST data presents inherent challenges, such as variability in image quality, artifacts from fiducial markers, and difficulty in distinguishing tissue regions. These challenges can significantly impair the accuracy and effectiveness of downstream analyses. To address these limitations, we developed Vispro, an end-to-end, fully automated image processing tool tailored for ST data. Vispro integrates four key modules: fiducial marker detection, marker removal and image restoration, tissue region detection, and segmentation of disconnected tissue areas. These modules systematically enhance the quality of ST images, reducing artifacts and improving tissue segmentation accuracy. Furthermore, by improving the integrity of image data, we demonstrate that Vispro facilitates downstream analyses such as cell segmentation and image registration, empowering researchers to extract more detailed and accurate biological insights from complex tissue architectures.

A Bioengineered Cathepsin B-sensitive Gas Vesicle Nanosystem That Responds With Increased Gray-level Intensity of Ultrasound Biomicroscopic Images

ObjectivesThis work aimed to promote the interaction of a modified Gas Vesicle (GV) with Cathepsin B (CTSB) protease and analyzed their backscattered signal by Ultrasound (US). MethodsWe modified the sequence of the gene coding for GvpC to contain a CTSB cleavage and expressed the protein in an Escherichia coli recombinant system. The protein was purified and added to GVs preparations in which the original GvpC was removed (ΔGV), constituting the modified GV (GV*). Western Blot (WB) test was used to compare GVs with GvpC and engineer GvpC at starting (T0) and after 24 hours (T24) reacting with CTSB. A 21 MHz US B-mode and nonlinear contrast-mode (5% total power) imaged US phantoms having samples of GVwt, ΔGV (stripped GV), GV*, and CTSB+GV*. Also, a 21MHz US B-mode imaged US phantoms having a tumor cell line extracellular fraction (TCEF) and the TCEF+GV* sample. A 100% total US power was applied to collapse the GV structure. ResultsIn WB we detected a decrease in engineered GvpC levels 24 h after the incubation of GV* with CTSB, compared to the concentration at T0, suggesting that CTSB cleaved the engineered GvpC. Regions-of-interest over image of phantom cross-sections were determined and the B-mode image mean gray level intensity resulted in a significant (p<0.05) increase comparing CTSB+GV* with PBS (control), GVwt, ΔGV and GV*. Non-linear mode image gray level intensity from CTSB+GV* increased by 11.79, 7.86 and 14.75 dB from samples containing GVwt, ΔGV and GV*, respectively. GV preparations incubated with TCEF and the TCEF+GV* sample showed an increase of 81% in signal compared to TCEF+GVwt. ConclusionsThe increased ultrasound backscattered signal intensity suggests GVs as a potential biosensor for protease activity, possibly aiding the detection of protease-rich tissue regions.

B-213 Including Turner Syndrome Screening in Newborn Sequencing by Using NGS Read Counts to Infer Sex Chromosomes Copy Number: Leveraging Existing Data to Enhance Early Diagnosis

Abstract Background Turner Syndrome (TS) can lead to heart defects, short stature, and premature ovarian failure. Early diagnosis can improve outcomes, but routine screening is not standard, often delaying diagnosis until symptoms prompt karyotype analysis. Newborn NGS sequencing offers potential for early detection of genetic disorders and targets numerous gene regions on autosomes and sex chromosomes. It could be utilized to deduce sexual chromosomal copy number variations, including not only chrX monosomy but also its partial deletions and structural alterations, and chrY, which increases the risk of gonadoblastoma in TS patients. Thus, this study aims to validate a TS screening approach using NGS read count data from newborn sequencing to develop a strategy for early detection. Methods DNA samples with established chromosomal microarray ChrX and ChrY copy-number assessments were used. The cohort included 13 males (46,XY), 10 females (46,XX), 5 TS cases (45,X), 5 mosaic TS cases (2 with chrY, 3 without), 1 TS case with Isochromosome Xq, 8 Klinefelter Syndrome cases (47,XXY), and one case (48,XXYY). For NGS, a focused newborn sequencing panel targeting 388 genes derived from the larger xGEN Inherited Disease Panel (Integrated DNA Technologies) was employed. The larger panel covers 2,968 chrX regions, 53,455 autosomal regions, and 135 chrY regions. Sample processing utilized the xGEN DNA Library Prep and Hybridization Capture (Integrated DNA Technologies) and sequencing on NextSeq-500 (Illumina). The alignment was conducted with Dragen Enrichment v.3.9.5 and read count analysis with Samtools v.1.19.2. ChrX and ChrY copy numbers were assessed by comparing chrX and chrY read count ratios to autosomal read counts. Hierarchical clustering grouped samples based on ratio similarities, aiding result attribution. To identify structural alterations in each arm of chrX, every tested chrX region was analyzed for read count ratio calculation and subjected to circular binary segmentation using the DNAcopy R package. The observed sex chromosome counts were compared to expected results to validate the approach. Results Observed chrX/autosome and chrY/autosome ratio ranges matched the expected sex chromosome counts: 0.99-1.03 and 0.82-1.02 for 46,XY; 1.92-1.97 and 0.00-0.01 for 46,XX; 1.00-1.02 and 0.00-0.00 for 45,X; 1.15-1.46 and 0.00-0.00 for mosaic TS without chrY; 0.98-1.06 and 0.27-0.32 for mosaic TS with chrY; 2.45 and 0.0 for TS with Isochromosome Xq; 1.82-2.01 and 0.87-0.95 for 47,XXY; and 2.02 and 1.96 for 48,XXYY. Hierarchical cluster analysis grouped these values into three branches: one for 48,XXYY, one for 47,XXY, 46,XX, and Isochromosome Xq, and one for all other TS samples and 46,XY. TS and 47,XXY samples were further divided into subbranches from 46,XX and 46,XY. Circular binary segmentation of chrX read counts provided a visual profile of the copy number for each arm of chrX, explaining structural alterations in out-of-pattern chrX dosages, such as 2.45, 1.15, and 1.46. Conclusions There was complete agreement between the NGS read count-based assessment of sex chromosome copy number and the expected results for 46,XY, 46,XX, 45,X, TS with mosaic and structural variants, 47,XXY, and 48,XXYY samples. This suggests that TS can be effectively screened using newborn sequencing NGS data.

Novel Genes of the Male Reproductive System: Potential Roles in Male Reproduction and as Non-hormonal Male Contraceptive Targets.

The development of novel non-hormonal male contraceptives represents a pivotal frontier in reproductive health, driven by the need for safe, effective, and reversible contraceptive methods. This comprehensive review explores the genetic underpinnings of male fertility, emphasizing the crucial roles of specific genes and structural variants (SVs) identified through advanced sequencing technologies such as long-read sequencing (LRS). LRS has revolutionized the detection of structural variants and complex genomic regions, offering unprecedented precision and resolution over traditional next-generation sequencing (NGS). Key genetic targets, including those implicated in spermatogenesis and sperm motility, are highlighted, showcasing their potential as non-hormonal contraceptive targets. The review delves into the systematic identification and validation of male reproductive tract-specific genes, utilizing advanced transcriptomics and genomics studies with validation using novel knockout mouse models. We discuss the innovative application of small molecule inhibitors, developed through platforms like DNA-encoded chemistry technology (DEC-Tec), which have shown significant promise in preclinical models. Notable examples include inhibitors targeting serine/threonine kinase 33 (STK33), soluble adenylyl cyclase (sAC), cyclin-dependent kinase 2 (CDK2), and bromodomain testis associated (BRDT), each demonstrating nanomolar affinity and potential for reversible and specific inhibition of male fertility. This review also honors the contributions of Dr. David L. Garbers whose foundational work has paved the way for these advancements. The integration of genomic, proteomic, and chemical biology approaches, supported by interdisciplinary collaboration, is poised to transform male contraceptive development. Future perspectives emphasize the need for continued innovation and rigorous testing to bring these novel contraceptives from the laboratory to clinical application, promising a new era of male reproductive health management.

Radiomic detection of abnormal brain regions in tuberous sclerosis complex.

Radiomics refers to the extraction of quantitative information from medical images and is most commonly utilized in oncology to provide ancillary information for solid tumor diagnosis, prognosis, and treatment response. The traditional radiomic pipeline involves segmentation of volumes of interest with comparison to normal brain. In other neurologic disorders, such as epilepsy, lesion delineation may be difficult or impossible due to poor anatomic definition, small size, and multifocal or diffuse distribution. Tuberous sclerosis complex (TSC) is a rare genetic disease in which brain magnetic resonance imaging (MRI) demonstrates multifocal abnormalities with variable imaging and epileptogenic features. The purpose of this study was to develop a radiomic workflow for identification of abnormal brain regions in TSC, using a whole-brain atlas-based approach with generation of heatmaps based on signal deviation from normal controls. This was a retrospective pilot study utilizing high-resolution whole-brain 3D FLAIR MRI datasets from retrospective enrollment of tuberous sclerosis complex (TSC) patients and normal controls. Subjects underwent MRI including high-resolution 3D FLAIR sequences. Preprocessing included skull stripping, coregistration, and intensity normalization. Using the Brainnetome and Harvard-Oxford atlases, brain regions were parcellated into 318 discrete regions. Expert neuroradiologists spatially labeled all tubers in TSC patients using ITK-SNAP. The pyradiomics toolbox was used to extract 88 radiomic features based on IBSI guidelines, comparing tuber-affected and non-tuber-affected parenchyma in TSC patients, as well as normal brain tissue in control patients. For model training and validation, regions with tubers from 20 TSC patients and 30 normal control subjects were randomly divided into two training sets (80%) and two validation sets (20%). Additional model testing was performed on a separate group of 20 healthy controls. LASSO (least absolute shrinkage and selection operator) was used to perform variable selection and regularization to identify regions containing tubers. Relevant radiomic features selected by LASSO were combined to produce a radiomic score ω, defined as the sum of squared differences from average control group values. Region-specific ω scores were converted to heat maps and spatially coregistered with brain MRI to reflect overall radiomic deviation from normal. The proposed radiomic workflow allows for quantification of deviation from normal in 318 regions of the brain with the use of a summative radiomic score ω. This score can be used to generate spatially registered heatmaps to identify brain regions with radiomic abnormalities. The pilot study of TSC showed radiomic scores ω that were statistically different in regions containing tubers from regions without tubers/normal brain (p<0.0001). Our model exhibits an AUC of 0.81 (95% confidence interval: 0.78-0.84) on the testing set, and the best threshold obtained on the training set, when applied to the testing set, allows us to identify regions with tubers with a specificity of 0.91 and a sensitivity of 0.60. We describe a whole-brain atlas-based radiomic approach to identify abnormal brain regions in TSC patients. This approach may be helpful for identifying specific regions of interest based on relatively greater signal deviation, particularly in clinical scenarios with numerous or poorly defined anatomic lesions.

Automated cardiac coverage assessment in cardiovascular magnetic resonance imaging using an explainable recurrent 3D dual-domain convolutional network.

Cardiovascular magnetic resonance (CMR) imaging has become a modality with superior power for the diagnosis and prognosis of cardiovascular diseases. One of the essential quality controls of CMR images is to investigate the complete cardiac coverage, which is necessary for the volumetric and functional assessment. This study examines the full cardiac coverage using a 3D dual-domain convolutional model and then improves this model using an innovative explainable salient region detection model and a recurrent architecture. Salient regions are extracted from the short-axis cine CMR stacks using a three-step proposed algorithm. Changing the architecture of the 3D dual-domain convolutional model to a recurrent one and taking advantage of the salient region detection model creates a kind of attention mechanism that leads to improved results. The results obtained from the images of over 6200 participants of the UK Biobank population cohort study show the superiority of the proposed model over the previous studies. The dataset is the largest regarding the number of participants to control the cardiac coverage. The accuracies of the proposed model in identifying the presence/absence of basal/apical slices are 96.22% and 95.42%, respectively. The proposed recurrent architecture of the 3D dual-domain convolutional model can force the model to focus on the most informative areas of the images using the extracted salient regions, which can help the model improve accuracy. The performance of the proposed fully automated model indicates that it can be used for image quality control in population cohort datasets and real-time post-imaging quality assessments. Codes are available at https://github.com/mohammadhashemii/CMR_Cardiac_Coverage_Control.

Microcanonical Monte Carlo of Lennard-Jones microclusters.

A novel statistical mechanical methodology is applied to clusters of N ≤ 7 atoms. Exact statistical analogs for any energy derivative of entropy ∂mS/∂Em are used in rigorous microcanonical Monte Carlo simulations to vastly enlarge the pool of measurable thermodynamic properties relative to previous work. All analogs are given for two alternative partition functions of the microcanonical ensemble. Coarse grained energy distributions are used to establish the existence of melting transitions. LJ7, LJ5, and LJ4 are found to exhibit trimodal distributions, a feature not being reported before. Varieties of combinations of entropy derivatives are tested for a direct detection of the melting region. It is shown that for such a purpose, derivatives of at least fourth order are necessary.

Automated detection of regions with persistently enhanced methane concentrations using Sentinel-5 Precursor satellite data

Abstract. Methane (CH4) is an important anthropogenic greenhouse gas, and its rising concentration in the atmosphere contributes significantly to global warming. A comparatively small number of highly emitting persistent methane sources are responsible for a large share of global methane emissions. The identification and quantification of these sources, which often show large uncertainties regarding their emissions or locations, are important to support mitigating climate change. Daily global column-averaged dry air mole fractions of atmospheric methane (XCH4) are retrieved from radiance measurements of the TROPOspheric Monitoring Instrument (TROPOMI) on board on the Sentinel-5 Precursor (S5P) satellite with a moderately high spatial resolution, enabling the detection and quantification of localized methane sources. We developed a fully automated algorithm to detect regions with persistent methane enhancement and to quantify their emissions using a monthly TROPOMI XCH4 dataset from the years 2018–2021. We detect 217 potential persistent source regions (PPSRs), which account for approximately 20 % of the total bottom-up emissions. By comparing the PPSRs in a spatial analysis with anthropogenic and natural emission databases, we conclude that 7.8 % of the detected source regions are dominated by coal, 7.8 % by oil and gas, 30.4 % by other anthropogenic sources like landfills or agriculture, 7.3 % by wetlands, and 46.5 % by unknown sources. Many of the identified PPSRs are in well-known source regions, like the Permian Basin in the USA, which is a large production area for oil and gas; the Bowen Basin coal mining area in Australia; or the Pantanal Wetlands in Brazil. We perform a detailed analysis of the PPSRs with the 10 highest emission estimates, including the Sudd Wetland in South Sudan, an oil- and gas-dominated area on the west coast in Turkmenistan, and one of the largest coal production areas in the world, the Kuznetsk Basin in Russia. The calculated emission estimates of these source regions are in agreement within the uncertainties in results from other studies but are in most of the cases higher than the emissions reported by emission databases. We demonstrate that our algorithm is able to automatically detect and quantify persistent localized methane sources of different source type and shape, including larger-scale enhancements such as wetlands or extensive oil- and gas-production basins.

Computed tomography radiomics-based cross-sectional detection of mandibular osteoradionecrosis in head and neck cancer survivors.

This study aims to identify radiomic features extracted from contrast-enhanced CT scans that differentiate osteoradionecrosis (ORN) from normal mandibular bone in patients with head and neck cancer (HNC) treated with radiotherapy (RT). Contrast-enhanced CT (CECT) images were collected for 150 patients (80% train, 20% test) with confirmed ORN diagnosis at The University of Texas MD Anderson Cancer Center between 2008 and 2018. Using PyRadiomics, radiomic features were extracted from manually segmented ORN regions and the corresponding automated control regions, the later defined as the contralateral healthy mandible region. A subset of pre-selected features was obtained based on correlation analysis (r > 0.95) and used to train a Random Forest (RF) classifier with Recursive Feature Elimination. Model explainability SHapley Additive exPlanations (SHAP) analysis was performed on the 20 most important features identified by the trained RF classifier. From a total of 1316 radiomic features extracted, 810 features were excluded due to high collinearity. From a set of 506 pre-selected radiomic features, the optimal subset resulting on the best discriminative accuracy of the RF classifier consisted of 67 features. The RF classifier was well calibrated (Log Loss 0.296, ECE 0.125) and achieved an accuracy of 88% and a ROC AUC of 0.96. The SHAP analysis revealed that higher values of Wavelet-LLH First-order Mean and Median were associated with ORN of the jaw (ORNJ). Conversely, higher Exponential GLDM Dependence Entropy and lower Square First-order Kurtosis were more characteristic of normal mandibular tissue. This study successfully developed a CECT-based radiomics model for differentiating ORNJ from healthy mandibular tissue in HNC patients after RT. Future work will focus on the detection of subclinical ORNJ regions to guide earlier interventions.

Snow Detection in Gaofen-1 Multi-Spectral Images Based on Swin-Transformer and U-Shaped Dual-Branch Encoder Structure Network with Geographic Information

Snow detection is imperative in remote sensing for various applications, including climate change monitoring, water resources management, and disaster warning. Recognizing the limitations of current deep learning algorithms in cloud and snow boundary segmentation, as well as issues like detail snow information loss and mountainous snow omission, this paper presents a novel snow detection network based on Swin-Transformer and U-shaped dual-branch encoder structure with geographic information (SD-GeoSTUNet), aiming to address the above issues. Initially, the SD-GeoSTUNet incorporates the CNN branch and Swin-Transformer branch to extract features in parallel and the Feature Aggregation Module (FAM) is designed to facilitate the detail feature aggregation via two branches. Simultaneously, an Edge-enhanced Convolution (EeConv) is introduced to promote snow boundary contour extraction in the CNN branch. In particular, auxiliary geographic information, including altitude, longitude, latitude, slope, and aspect, is encoded in the Swin-Transformer branch to enhance snow detection in mountainous regions. Experiments conducted on Levir_CS, a large-scale cloud and snow dataset originating from Gaofen-1, demonstrate that SD-GeoSTUNet achieves optimal performance with the values of 78.08%, 85.07%, and 92.89% for IoU_s, F1_s, and MPA, respectively, leading to superior cloud and snow boundary segmentation and thin cloud and snow detection. Further, ablation experiments reveal that integrating slope and aspect information effectively alleviates the omission of snow detection in mountainous areas and significantly exhibits the best vision under complex terrain. The proposed model can be used for remote sensing data with geographic information to achieve more accurate snow extraction, which is conducive to promoting the research of hydrology and agriculture with different geospatial characteristics.

JADES. The diverse population of infant black holes at 4&lt;z&lt;11: Merging, tiny, poor, but mighty

Spectroscopy with the James Webb Space Telescope has opened the possibility of identifying moderate-luminosity active galactic nuclei (AGNs) in the early Universe, at and beyond the epoch of re-ionisation, complementing previous surveys of much more luminous (and much rarer) quasars. We present 12 new AGNs at 4$&lt;$z$&lt;$7 in the JADES survey (in addition to the previously identified AGN in GN-z11 at z=10.6) revealed through the detection of a broad-line region (BLR) seen in the Balmer emission lines. The depth of JADES, together with the use of three different spectral resolutions, enables us to probe a lower-mass regime relative to previous studies. In a few cases, we find evidence for two broad components of Halpha , which suggests that these could be candidate merging black holes (BHs), although a complex BLR geometry cannot be excluded. The inferred BH masses range from $ 10^7 M_ down to $ 10^5 M_ interestingly probing the regime expected for direct collapse BHs. The inferred AGN bolometric luminosities ($ $ erg/s) imply accretion rates that are $&lt; 0.5$ times the Eddington rate in most cases. However, small BHs, with $ BH 10^6 M_ tend to accrete at Eddington or super-Eddington rates. These BHs at zsim 4-11 are over-massive relative to their host galaxies' stellar masses when compared to the local $ BH -M_ star $ relation, even approaching $ BH star $, as was expected from heavy BH seeds and/or super-Eddington accretion scenarios. However, we find that these early BHs tend to be more consistent with the local relation between $ BH $ and velocity dispersion, as well as between $ BH $ and dynamical mass, suggesting that these are more fundamental and universal relations. On the classical, optical narrow-line excitation-diagnostic diagrams, these AGNs are located in the region that is that is locally occupied by star-forming galaxies, implying that they would be missed by the standard classification techniques if they did not display broad lines. Their location on the diagram is consistent with what is expected for AGNs hosted in metal-poor galaxies ($ Z 0.1-0.2 Z_ The fraction of broad-line AGNs with $ AGN erg/s$ among galaxies in the redshift range of $4&lt;z&lt;6$ is about 10&lt;!PCT!&gt;, suggesting that the contribution of AGNs and their hosts to the re-ionisation of the Universe is $&gt;$10&lt;!PCT!&gt;.

Using 3D U-Net for Brain Tumour Segmentation from Magnetic Resonance Images

Brain tumours within the skull can lead to serious health issues. The rapid and accurate detection and segmentation of tumour regions allow patients to receive appropriate treatment at an early stage, increasing their chances of recovery and survival. Various medical imaging methods, such as Magnetic Resonance Imaging (MRI), Positron and digital pathology, Emission Tomography (PET), Computed Tomography (CT) are used for the detection of brain tumours. Nowadays, with advancing technology and hardware, concepts like artificial intelligence and deep learning (DL) are becoming increasingly popular. Many artificial intelligence methods are also being utilized in studies on brain tumour segmentation. This paper proposes a 3D U-Net DL model for brain tumour segmentation. The training and testing processes are carried out on the Brain Tumour Segmentation (BraTS) 2020 dataset, which is widely used in the literature. As a result, an Intersection over Union (IoU) score of 0.81, a dice score of 0.87 and a pixel accuracy of 0.99 are achieved. The proposed model has the potential to assist experts in diagnosing the disease and developing appropriate treatment plans, thanks to its ability to segment brain tumours quickly and with high accuracy.

A genetic programming approach with adaptive region detection to skin cancer image classification

Dermatologists typically require extensive experience to accurately classify skin cancer. In recent years, the development of computer vision and machine learning has provided new methods for assisted diagnosis. Existing skin cancer image classification methods have certain limitations, such as poor interpretability, the requirement of domain knowledge for feature extraction, and the neglect of lesion area information in skin images. This paper proposes a new genetic programming (GP) approach to automatically learn global and/or local features from skin images for classification. To achieve this, a new function set and a new terminal set have been developed. The proposed GP method can automatically and flexibly extract effective local/global features from different types of input images, thus providing a comprehensive description of skin images. A new region detection function has been developed to select the lesion areas from skin images for feature extraction. The performance of this approach is evaluated on three skin cancer image classification tasks, and compared with three GP methods and six non-GP methods. The experimental results show that the new approach achieves significantly better or similar performance in most cases. Further analysis validates the effectiveness of our parameter settings, visualizes the multiple region detection functions used in the individual evolved by the proposed approach, and demonstrates its good convergence ability.

Computer Augmented Reality Model-Based Interactive Teaching System for Chinese Line Drawing CAD

To achieve automatic target region detection, an interactive image segmentation technique has been always used as preferences. However, when it comes to the implementation of computer aided design, there is a problem with the structured method. The technique of stroke contour extraction based on boundary detection is used in this investigation. The high-frequency lifting filter improves the original image, and the grey scale and spatial distribution of the pixels are used to determine how the pixels are distributed on the pen track. The segmentation of mural lines and strokes is achieved well in a limited space. To ascertain each pixel's grey value distribution, a non-realistic rendering technique is suggested. Draw in accordance with the boundary tangential flow and each pixel's category. In conclusion, the two approaches can cover more application scenarios and yield good results in the extraction of creative materials from Chinese classical mural painting while maintaining the entire shape of the original strokes in the painting.