Archival Images Research Articles

A Novel Semantic Content-Based Retrieval System for Hyperspectral Remote Sensing Imagery

With the growing use of hyperspectral remote sensing payloads, there has been a significant increase in the number of hyperspectral remote sensing image archives, leading to a massive amount of collected data. This highlights the need for an efficient content-based hyperspectral image retrieval (CBHIR) system to manage and enable better use of hyperspectral remote-sensing image archives. Conventional CBHIR systems characterize each image by a set of endmembers and then perform image retrieval based on pairwise distance measures. Such an approach significantly increases the computational complexity of the retrieval, mainly when the diversity of materials is high. Those systems also have difficulties in retrieving images containing particular materials with extremely low abundance compared to other materials, which leads to describing image content with inappropriate and/or insufficient spectral features. In this article, a novel CBHIR system to define global hyperspectral image representations based on a semantic approach to differentiate foreground and background image content for different retrieval scenarios is introduced to address these issues. The experiments conducted on a new benchmark archive of multi-label hyperspectral images, which is first introduced in this study, validate the retrieval accuracy and effectiveness of the proposed system. Comparative performance analysis with the state-of-the-art CBHIR systems demonstrates that modeling hyperspectral image content with foreground and background vocabularies has a positive effect on retrieval performance.

Searching for precursor activity of Type IIn Supernovae

We conducted a search for luminous outbursts prior to the explosion of Type IIn supernovae (SNe IIn). We built a sample of 27 objects spectroscopically classified as SNe IIn and all located at $z<0.015$. Using deep archival SN fields images ---taken up to nearly 20 years prior to the SN explosions themselves--- from transient surveys (PTF, ZTF, DES and CHASE) and major astronomical observatories (European Southern Observatory, ESO and National Optical Astronomy Observatory, NOAO), we found at least one outburst years to months before the explosion of seven SNe IIn, with the earliest precursor being 10 years prior to the explosion of SN 2019bxq. The maximum absolute magnitudes of the outbursts range between $-$11.5 mag and $-$15 mag, and the eruptive phases last for a few weeks to a few years. The $g-r$ colour measured for three objects during their outburst is relatively red, with $g-r$ ranging between 0.5 and 1.0 mag. This is similar to the colour expected during the eruptions of luminous blue variables. We note that the light curves of SNe with pre-SN outbursts have faster decline rates than those of the SNe that do not show pre-SN outbursts. SN 2011fh is remarkable, as it is still visible 12 years after the luminous SN-like event, indicating that the progenitor possibly survived, or that the interaction is still ongoing. We detect precursor activity in 29<!PCT!> of bona fide SNe IIn in our sample. However, a quantitative assessment of the observational biases affecting the sample suggests that this fraction is an underestimation of the intrinsic precursor occurrence rate.

Lymph node metastasis prediction and biological pathway associations underlying DCE-MRI deep learning radiomics in invasive breast cancer.

The relationship between the biological pathways related to deep learning radiomics (DLR) and lymph node metastasis (LNM) of breast cancer is still poorly understood. This study explored the value of DLR based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in LNM of invasive breast cancer. It also analyzed the biological significance of DLR phenotype based on genomics. Two cohorts from the Cancer Imaging Archive project were used, one as the training cohort (TCGA-Breast, n = 88) and one as the validation cohort (Breast-MRI-NACT Pilot, n = 57). Radiomics and deep learning features were extracted from preoperative DCE-MRI. After dual selection by principal components analysis (PCA) and relief methods, radiomics and deep learning models for predicting LNM were constructed by the random forest (RF) method. A post-fusion strategy was used to construct the DLR nomograms (DLRNs) for predicting LNM. The performance of the models was evaluated using the receiver operating characteristic (ROC) curve and Delong test. In the training cohort, transcriptome data were downloaded from the UCSC Xena online database, and biological pathways related to the DLR phenotypes were identified. Finally, hub genes were identified to obtain DLR gene expression (RadDeepGene) scores. DLRNs were based on area under curve (AUC) evaluation (training cohort, AUC = 0.98; validation cohort, AUC = 0.87), which were higher than single radiomics models or GoogLeNet models. The Delong test (radiomics model, P = 0.04; GoogLeNet model, P = 0.01) also validated the above results in the training cohorts, but they were not statistically significant in the validation cohort. The GoogLeNet phenotypes were related to multiple classical tumor signaling pathways, characterizing the biological significance of immune response, signal transduction, and cell death. In all, 20 genes related to GoogLeNet phenotypes were identified, and the RadDeepGene score represented a high risk of LNM (odd ratio = 164.00, P < 0.001). DLRNs combining radiomics and deep learning features of DCE-MRI images improved the preoperative prediction of LNM in breast cancer, and the potential biological characteristics of DLRN were identified through genomics.

Stage and discharge prediction from documentary time-lapse imagery

Imagery from fixed, ground-based cameras is rich in qualitative and quantitative information that can improve stream discharge monitoring. For instance, time-lapse imagery may be valuable for filling data gaps when sensors fail and/or during lapses in funding for monitoring programs. In this study, we used a large image archive (&gt;40,000 images from 2012 to 2019) from a fixed, ground-based camera that is part of a documentary watershed imaging project (https://plattebasintimelapse.com/). Scalar image features were extracted from daylight images taken at one-hour intervals. The image features were fused with United States Geological Survey stage and discharge data as response variables from the site. Predictions of stage and discharge for simulated year-long data gaps (2015, 2016, and 2017 water years) were generated from Multi-layer Perceptron, Random Forest Regression, and Support Vector Regression models. A Kalman filter was applied to the predictions to remove noise. Error metrics were calculated, including Nash-Sutcliffe Efficiency (NSE) and an alternative threshold-based performance metric that accounted for seasonal runoff. NSE for the year-long gap predictions ranged from 0.63 to 0.90 for discharge and 0.47 to 0.90 for stage, with greater errors in 2016 when stream discharge during the gap period greatly exceeded discharge during the training periods. Importantly, and in contrast to gap-filling methods that do not use imagery, the high discharge conditions in 2016 could be visually (qualitatively) verified from the image data. Half-year test sets were created for 2016 to include higher discharges in the training sets, thus improving model performance. While additional machine learning algorithms and tuning parameters for selected models should be tested further, this study demonstrates the potential value of ground-based time-lapse images for filling large gaps in hydrologic time series data. Cameras dedicated for hydrologic sensing, including nighttime imagery, could further improve results.

Exploring celiac trunk parameters in median arcuate ligament syndrome: A CT study.

To assess anatomical variations in the celiac trunk (Ct) in patients with Median Arcuate Ligament Syndrome (MALS) using computed tomography (CT). The primary objectives were to investigate the celiac trunk angle (CtA), origin level, length (CtL), and their relationships with the superior mesenteric artery (SMA) in MALS patients. Additionally, the study intended to evaluate gender differences in these parameters and explore correlations between variables. Retrospectively, reports of abdominal CT scans taken between January 2018, and Sepmtember 2021, in the hospital image archive were screened vey two observers independently for MALS diagnosis. Parameters such as CtA, CtL, Ct-SMA distance, SMA angle (SMAA), and median arcuate ligament thickness (MALT) were measured. Statistical analyses were conducted using SPSS software. Among the 81 patients (25 females, 56 males), significant differences were observed in MALT between genders (p = 0.001). CtA showed a negative correlation with CtL and Ct-SMA (p < 0.001), and a positive correlation was found between CtL and Ct-SMA (p = 0.002). CtL was measured as 25mm for the all group. Origin levels of Ct and SMA were evaluated in comparison to vertebral levels. Ct-SMA distance was relatively shorter (9.19mm) compared to the literature. SMAA findings were consistent with normal population values. This study provided valuable insights into the anatomical parameters of the Ct ans SMA in MALS patients. Despite some differences compared to normal population parameters, no evidence supported the hypothesis of a superiorly placed Ct contributing to MALS.

Simulation of time-intensity curve based on k-space filling in breast dynamic contrast-enhanced three-dimensional magnetic resonance imaging.

This study elucidated the effects of a three-dimensional k-space trajectory incorporating the partial Fourier (PF) technique on a time-intensity curve (TIC) in a dynamic contrast-enhanced magnetic resonance imaging of a typical malignant breast tumor using a digital phantom. Images were obtained from the Cancer Imaging Archive Open Data for Breast Cancer, and 1-min scans with high temporal resolution were analyzed. The order of the k-space trajectory was set as Linear (sequential), Low-High (centric), PF (62.5%; Z-, Y-, and both directions), and Low-High Radial. k0 (center of the k-space) timing and TIC shape were affected by the chosen k-space trajectory and implementation of the PF technique. A small TIC gradient was obtained using a Low-High Radial order. If the k-space filling method (particularly the radial method) produces a gentle TIC gradient, misinterpretation could arise during the assessment of tumor malignancy status.

Exploring tumor heterogeneity in colorectal liver metastases by imaging: Unsupervised machine learning of preoperative CT radiomics features for prognostic stratification

ObjectivesThis study aimed to investigate tumor heterogeneity of colorectal liver metastases (CRLM) and stratify the patients into different risk groups of prognoses following liver resection by applying an unsupervised radiomics machine-learning approach to preoperative CT images. MethodsThis retrospective study retrieved clinical information and CT images of 197 patients with CRLM from The Cancer Imaging Archive (TCIA) database. Radiomics features were extracted from a segmented liver lesion identified at the portal venous phase. Those features which showed high stability, non-redundancy, and indicative information were selected. An unsupervised consensus clustering analysis on these features was adopted to identify subgroups of CRLM patients. Overall survival (OS), disease-free survival (DFS), and liver-specific DFS were compared between the identified subgroups. Cox regression analysis was applied to evaluate prognostic risk factors. ResultsA total of 851 radiomics features were extracted, and 56 robust features were finally selected for unsupervised clustering analysis which identified two distinct subgroups (96 and 101 patients respectively). There were significant differences in the OS, DFS, and liver-specific DFS between the subgroups (all log-rank p < 0.05). The subgroup with worse outcome using the proposed radiomics model was consistently associated with shorter OS, DFS, and liver-specific DFS, with hazard ratios of 1.78 (95 %CI: 1.12–2.83), 1.72 (95 %CI: 1.16–2.54), and 1.59 (95 %CI: 1.10–2.31), respectively. The general performance of this radiomics model outperformed the traditional Clinical Risk Score and Tumor Burden Score in the prognosis prediction after surgery for CRLM. ConclusionRadiomics features derived from preoperative CT images can reveal the heterogeneity of CRLM and stratify the patients with CRLM into subgroups with significantly different clinical outcomes.

Public data homogenization for AI model development in breast cancer.

Developing trustworthy artificial intelligence (AI) models for clinical applications requires access to clinical and imaging data cohorts. Reusing of publicly available datasets has the potential to fill this gap. Specifically in the domain of breast cancer, a large archive of publicly accessible medical images along with the corresponding clinical data is available at The Cancer Imaging Archive (TCIA). However, existing datasets cannot be directly used as they are heterogeneous and cannot be effectively filtered for selecting specific image types required to develop AI models. This work focuses on the development of a homogenized dataset in the domain of breast cancer including clinical and imaging data. Five datasets were acquired from the TCIA and were harmonized. For the clinical data harmonization, a common data model was developed and a repeatable, documented "extract-transform-load" process was defined and executed for their homogenization. Further, Digital Imaging and COmmunications in Medicine (DICOM) information was extracted from magnetic resonance imaging (MRI) data and made accessible and searchable. The resulting harmonized dataset includes information about 2,035 subjects with breast cancer. Further, a platform named RV-Cherry-Picker enables search over both the clinical and diagnostic imaging datasets, providing unified access, facilitating the downloading of all study imaging that correspond to specific series' characteristics (e.g., dynamic contrast-enhanced series), and reducing the burden of acquiring the appropriate set of images for the respective AI model scenario. RV-Cherry-Picker provides access to the largest, publicly available, homogenized, imaging/clinical dataset for breast cancer to develop AI models on top. We present a solution for creating merged public datasets supporting AI model development, using as an example the breast cancer domain and magnetic resonance imaging images. • The proposed platform allows unified access to the largest, homogenized public imaging dataset for breast cancer. • A methodology for the semantically enriched homogenization of public clinical data is presented. • The platform is able to make a detailed selection of breast MRI data for the development of AI models.

Current Use and Barriers to Point-of-Care Ultrasound in Rheumatology: A National Survey of VA Medical Centers.

Point-of-care ultrasound (POCUS) can assist rheumatologists in monitoring disease activity, establishing diagnoses, and guiding procedural interventions. POCUS use has been increasing, but little is known about current use and barriers among rheumatologists.The purpose of this study was to characterize current POCUS use, training needs, and barriers to use among rheumatologists in practice. A prospective observational study of all Veterans Affairs (VA) medical centers was conducted using a web-based survey sent to all chiefs of staff and rheumatology chiefs about current POCUS use, training needs, barriers, and policies. All chiefs of staff (n = 130) and rheumatology chiefs at VA medical centers (n = 95) were surveyed with 100% and 84% response rates, respectively.The most common diagnostic POCUS applications were evaluation of synovitis, joint effusion, tendinopathies, bursitis, and rotator cuff. The most common procedural applications were arthrocentesis and joint, bursa, and tendon injection. Most rheumatology chiefs (69%) expressed interest in training for their group. The most common barriers to POCUS use were lack of trained providers (68%), funding for training (54%), training opportunities (38%), funding for travel (38%), and ultrasound equipment (31%). Lack of POCUS infrastructure was common, and few facilities had POCUS policies (20%), image archiving (25%), or quality assurance processes (6%). Currently, half of rheumatology groups use diagnostic and procedural ultrasound applications. Most rheumatology groups desire training, and lack of training and equipment were the most common barriers to ultrasound use. Deliberate investment is needed in ultrasound training and infrastructure for systematic adoption of POCUS in rheumatology. Graphical Abstractavailable for this article. NCT03296280.

A Spectroscopic Characterization of a Blazar Candidate Excluded by Automated Morphology Classification

Blazars are radio-loud Active Galactic Nuclei (AGNs) with their jets pointing at us at a very small angle. J064334.60–331430.29 (hereafter J0643–3314) was selected as a blazar candidate by its colors and compactness in archival radio images. In Xie et al. 2024, we introduce an algorithm to recognize likely blazars using the morphology observed in the 3 GHz Very Large Array Sky Survey. According to our algorithm, J0643–3314 is unlikely a blazar based on the two-sided jet seen in VLASS. To validate the result of our algorithm, we investigate this source further. From the extended two-sided jet lobes, the upturn radio spectrum, and the broad emission lines in the follow-up spectrum, we robustly conclude that J0643–3314 is not a blazar. We spectroscopically confirm it as a radio-loud AGN at z = 0.3, with a projected jet size of 100.4 kpc. We estimate its bolometric luminosity to be ∼6×1044 erg s−1, black hole mass of ∼2×108 M ⊙, and Eddington ratio of ∼0.02 from Hβ and Hα broad emission lines.

Evaluation of the functional and radiologic outcomes in talus fractures following surgery.

The long-term outcome of talus fractures is not yet sufficiently favorable despite improved resources and growing experience. With increasing fracture severity, the complication rate increases. This study aimed to evaluate the mid-to-long-term clinical and radiologic outcomes using the scoring system and imaging archive in patients with talus fractures who were surgically treated in our hospital. The mid- to long-term outcomes of patients with talus fractures admitted to Aydın Adnan Menderes University Faculty of Medicine Hospital between January 2010 and December 2020 and treated surgically were analyzed using satisfaction and functional scoring systems. Demographic data of the patients enrolled in our study indicated that talus fractures primarily developed in young males (p<0.05). The scores obtained from American Orthopaedic Foot & Ankle Society (AOFAS) scoring were consistent with patients' long-term consequences, such as avascular necrosis and post-traumatic arthritis (p<0.05). The rates of avascular necrosis and post-traumatic arthritis were lower, whereas AOFAS scores were higher in patients in whom the reduction quality was within the exact anatomical limits (p<0.05). The Hawkins sign had a positive predictive significance in patients free of avascular necrosis (p<0.05). Higher AOFAS scores were observed in patients treated with a single surgical incision (p<0.05). The timing of the surgery did not influence the results (p>0.05). The outcomes of patients treated surgically for talus fracture depended on the quality of reduction. In the mid-to-long term, the satisfaction scores of our patients with talus fractures who had undergone surgical treatment were rated as moderate.

Solar Image Quality Assessment: A Proof of Concept Using Variance of Laplacian Method and its Application to Optical Atmospheric Condition Monitoring

Here we present a proof of concept for the application of the Variance of Laplacian (VL) method in quantifying the sharpness of optical solar images. We conducted a comprehensive study using over 65,000 individual solar images acquired on more than 160 days. Each image underwent processing using a VL image processing algorithm, which assigns a “score” based on the sharpness of the solar disk’s edges. We studied the scores obtained from images acquired at different conditions. Our findings demonstrate that the sharpness of the images exhibits daily trends that are closely linked to the altitude of the Sun at the observation site. We observed a significant degradation in image quality only below a certain altitude threshold. Furthermore, we compared airmass formulae from the literature with our sharpness observations and concluded that the degradation could be modeled as an Image Sharpness Function (ISF), which exhibits similarities to airmass variations. In addition to assessing image quality, our method has the potential to evaluate the optical atmospheric conditions during daytime observations. Moreover, this technique can be easily and cost-effectively applied to archival or real-time images of other celestial bodies, such as the Moon, bright planets and defocused stars. Given that ISF is unique to each location and sensitive to sky conditions, the development of an ISF is not only beneficial for routine observation preparation but also essential for long-term site monitoring.

A Tale of Three Dwarfs: Cluster-based Star Formation Histories of Blue Compact Dwarf Galaxies

We present a new study of the cluster populations in the blue compact dwarf galaxies (BCD) ESO185-IG13, ESO338-IG04, and Haro11, based on new and archival high-resolution images taken by the Hubble Space Telescope, and the first to probe the populations older than ≈100 Myr. BCDs are believed to experience intense bursts of star formation (including at the present day) after long periods of quiescence, but little is known about the timing, frequency, duration, and strength of these bursts or about their star formation histories in general. We find that the cluster population in each of the three galaxies studied here has its own unique distribution of colors and hence a unique cluster and star formation history. From an assumed correlation between the normalization of the cluster mass function and the star formation rate of the host galaxy, we construct cluster-based star formation histories over the past ≈few × Gyr and find that only Haro11 is currently experiencing a burst (≈factor of 10 increase in the rate of star formation for the last ≈20 Myr), whereas ESO185 experienced enhanced star formation (by a factor ≈4) between 10 and 40 Myr ago, and ESO338 has had a fairly constant SFH over the past few Gyr. These findings indicate that not all BCDs are experiencing a burst of star formation at the present day, and that some have been forming stars and clusters at a fairly steady rate (within a factor of ≈2–3) over the past few Gyr. This scenario is similar to the histories of dwarf irregular and dwarf starburst galaxies, which have star formation rates that are 10–1000 times lower than those in BCDs.

CT-γ-Net: A Hybrid Model based on Convolutional Encoder-Decoder and Transformer Encoder for Brain Tumor Localization

Brain Tumor is a life-threatening disease, and its early diagnosis can save human life. Computer-Aided Brain Tumor Segmentation or Localization in Magnetic Resonance Imaging (MRI) images have emerged as pivotal approaches for expediting the disease diagnosis process. In the past few decades, various researchers combined the strengths of Convolutional Networks and Transformer to perform Brain Tumor Segmentation. However, these models require a large number of trainable weights parameters, and there is still scope for performance improvement in them. To bridge these research gaps, this paper proposes a novel hybrid model named “CT-γ-Net” for effective and efficient Brain Tumor Localization. The proposed CT-γ-Net model follows an Encoder-Decoder structure in which the Convolutional Encoder (CE) and Transformer Encoder (TE) are used for encoding, whereas the Convolutional Decoder (CD) is utilized for decoding the combined output of CE and TE to generate the segmentation masks. In CE and CD components of CT-γ-Net model conventional convolutional layers are replaced by Depth-Wise Separable convolutional layers, as these layers significantly reduce trainable weights parameters. The proposed model achieves 95.5% MeanIoU, 94.82% Dice Score, and 99.24% Pixel Accuracy on publicly available dataset named The Cancer Imaging Archive (TCIA). These experimental results demonstrate that the CT-γ-Net model outperformed other state-of-the-art research works, despite using roughly 28% fewer trainable weights parameters. Hence, the proposed model’s lightweight nature and its high performance, makes it a suitable candidate for deployment on mobile devices, facilitating the precise localization of brain tumor regions in MRI images.

234 Predictive Radiomics in Glioblastoma: A Machine Learning Approach for Personalized Survival Outcomes and a Novel Web Application

INTRODUCTION: Diverse characteristics of glioblastomas (GBMs) complicate survival predictions, requiring personalized approaches. METHODS: From The Cancer Imaging Archive (TCIA) database, 611 patients from the UPENN-GBM collection and 500 patients from the UCSF-PDGM collections were included in this study. UPENN-GBM collection was used as the training set and for 5-fold internal cross-validation, whereas the UCSF-PDGM collection was used for external validation. Survival at 3, 6, 12, and 24 months were the outcomes of interest. Necrotic tumor core, enhancing tumor, and whole tumor segmentation masks for T1 with contrast, T1, T2, and FLAIR images were obtained automatically with a pre-trained Swin UNETR model. From these regions of interest, radiomic features were extracted using the PyRadiomicsLibrary. The LASSO (least absolute shrinkage and selection operator) method was employed for radiomic feature selection. The ML algorithms TabPFN, XGBoost, LightGBM, Support Vector Machine (SVM), and Random Forest (RF) were utilized to predict the survival outcomes of interest. RESULTS: The highlight of our study, our web application is available for use via the following link: https://huggingface.co/spaces/MSHSNeurosurgeryResearch/Radiomics_Based_Glioblastoma_Survival_Prediction_Tool. CONCLUSIONS: This study not only provides a radiomics pipeline for predicting the GBM survival outcomes, but it also contributes to the clinical adoption of these models by developing a fully automatic and user-friendly web application which make them available to clinicians worldwide.

A radiomics signature derived from CT imaging to predict MSI status and immunotherapy outcomes in gastric cancer: a multi-cohort study.

Accurate microsatellite instability (MSI) testing is essential for identifying gastric cancer (GC) patients eligible for immunotherapy. We aimed to develop and validate a CT-based radiomics signature to predict MSI and immunotherapy outcomes in GC. This retrospective multicohort study included a total of 457 GC patients from two independent medical centers in China and The Cancer Imaging Archive (TCIA) databases. The primary cohort (n = 201, center 1, 2017-2022), was used for signature development via Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression analysis. Two independent immunotherapy cohorts, one from center 1 (n = 184, 2018-2021) and another from center 2 (n = 43, 2020-2021), were utilized to assess the signature's association with immunotherapy response and survival. Diagnostic efficiency was evaluated using the area under the receiver operating characteristic curve (AUC), and survival outcomes were analyzed via the Kaplan-Meier method. The TCIA cohort (n = 29) was included to evaluate the immune infiltration landscape of the radiomics signature subgroups using both CT images and mRNA sequencing data. Nine radiomics features were identified for signature development, exhibiting excellent discriminative performance in both the training (AUC: 0.851, 95%CI: 0.782, 0.919) and validation cohorts (AUC: 0.816, 95%CI: 0.706, 0.926). The radscore, calculated using the signature, demonstrated strong predictive abilities for objective response in immunotherapy cohorts (AUC: 0.734, 95%CI: 0.662, 0.806; AUC: 0.724, 95%CI: 0.572, 0.877). Additionally, the radscore showed a significant association with PFS and OS, with GC patients with a low radscore experiencing a significant survival benefit from immunotherapy. Immune infiltration analysis revealed significantly higher levels of CD8 + T cells, activated CD4 + B cells, and TNFRSF18 expression in the low radscore group, while the high radscore group exhibited higher levels of T cells regulatory and HHLA2 expression. This study developed a robust radiomics signature with the potential to serve as a non-invasive biomarker for GC's MSI status and immunotherapy response, demonstrating notable links to post-immunotherapy PFS and OS. Additionally, distinct immune profiles were observed between low and high radscore groups, highlighting their potential clinical implications.

A radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning and international multicohort study.

Axillary lymph nodes (ALN) status serves as a crucial prognostic indicator in breast cancer (BC). The aim of this study was to construct a radiogenomic multimodal model, based on machine learning and whole-transcriptome sequencing (WTS), to accurately evaluate the risk of ALN metastasis (ALNM), drug therapeutic response and avoid unnecessary axillary surgery in BC patients. In this study, conducted a retrospective analysis of 1078 BC patients from The Cancer Genome Atlas (TCGA), The Cancer Imaging Archive (TCIA), and Foshan cohort. These patients were divided into the TCIA cohort ( N =103), TCIA validation cohort ( N =51), Duke cohort ( N =138), Foshan cohort ( N =106), and TCGA cohort ( N =680). Radiological features were extracted from BC radiological images and differentially expressed gene expression was calibrated using technology. A support vector machine model was employed to screen radiological and genetic features, and a multimodal model was established based on radiogenomic and clinical pathological features to predict ALNM. The accuracy of the model predictions was assessed using the area under the curve (AUC) and the clinical benefit was measured using decision curve analysis. Risk stratification analysis of BC patients was performed by gene set enrichment analysis, differential comparison of immune checkpoint gene expression, and drug sensitivity testing. For the prediction of ALNM, rad-score was able to significantly differentiate between ALN- and ALN+ patients in both the Duke and Foshan cohorts ( P <0.05). Similarly, the gene-score was able to significantly differentiate between ALN- and ALN+ patients in the TCGA cohort ( P <0.05). The radiogenomic multimodal nomogram demonstrated satisfactory performance in the TCIA cohort (AUC 0.82, 95% CI: 0.74-0.91) and the TCIA validation cohort (AUC 0.77, 95% CI: 0.63-0.91). In the risk sub-stratification analysis, there were significant differences in gene pathway enrichment between high and low-risk groups ( P <0.05). Additionally, different risk groups may exhibit varying treatment responses ( P <0.05). Overall, the radiogenomic multimodal model employs multimodal data, including radiological images, genetic, and clinicopathological typing. The radiogenomic multimodal nomogram can precisely predict ALNM and drug therapeutic response in BC patients.

The detection and classification of acute myeloid leukaemia blood cell images based on different YOLO approaches

Medical image examination with a deep learning approach is greatly beneficial in the healthcare industry for faster diagnosis and disease monitoring. One of the popular deep learning algorithms such as you only look once (YOLO) developed for object detection is a successful state-ofthe-art algorithm in real-time object detection systems. Although YOLO is continuously improving in the object detection area, there are still questions about how different YOLO versions compare in terms of performance. We utilize eight YOLO versions to classify acute myeloid leukaemia (AML) blood cells in image examinations. We also acquired the publicly available AML dataset from the cancer imaging archive (TCIA) which consists of expert-labeled single cell images. Data augmentation techniques are additionally applied to enhance and balance the training images in the dataset. The overall results indicated that eight types of YOLO approaches have outstanding performances of more than 90% in precision and sensitivity. In comparison, YOLOv4-tiny has a more reliable performance than the other seven approaches. Consistently, the YOLOv4-tiny also achieved the highest AUC score. Therefore, this work can potentially provide a beneficial digital rapid tool in the screening and evaluation of numerous haematological disorders.

Multiband reflectance and shadowing of the protoplanetary disk RX J1604.3-2130 in scattered light

Context. A spatially resoved circumstellar disk spectrum and composition can provide valuable insights into the bulk composition of forming planets and into the mineralogical signatures that emerge during and after planet formation. Aims. We systemically extracted the RX J1604.3-213010 (J1604 hereafter) protoplanetary disk in high-contrast imaging observations and obtained its multi-band reflectance in the visible to near-infrared wavelengths. Methods. We obtained coronagraphic observations of J1604 from the Keck Observatory NIRC2 instrument and archival data from the Very Large Telescope SPHERE instrument. Using archival images to remove star light and speckles, we recovered the J1604 disk and obtained its surface brightness using forward modeling. Together with polarization data, we obtained the relative reflectance of the disk in R, J, H (H2 and H3), K (K1 and K2), and L′ bands spanning 2 yr. Results. Relative to the J1604 star, the resolved disk has a reflectance of ~10−1 arcsec−2 in R through H bands and ~10−2 arcsec−2 in K and L′ bands, showing a blue color. Together with other systems, we summarized the multiband reflectance for nine systems. We also identified a varying disk geometry structure, and a shadow that vanished between June and August in 2015. Conclusions. Motivated by broadband observations, the deployment of the latest technologies could yield higher-resolution reflection spectra, thereby informing the dust composition of disks in scattered light in the future. With multi-epoch observations, variable shadows have the potential to deepen our insight into the dynamic characteristics of inner disk regions.

DIGITAL IMAGE RESTORATION USING SURF ALGORITHM

In contemporary times, the preservation of scientific and creative endeavours often relies on the utilization of film and image archives, hence emphasizing the significance of image processing as a critical undertaking. Image inpainting refers to the process of digitally altering an image in a manner that renders the adjustments imperceptible to a viewer lacking knowledge of the original image. Image inpainting is a technique mostly employed to restore damaged regions within an image by utilizing information obtained from matching characteristics in relevant images. This process involves filling in the damaged areas and removing undesired objects. The SURF (Speeded Up Robust Feature) algorithm under consideration is partitioned into three primary phases. Firstly, the essential characteristics of the impaired image and the pertinent image are identified. In the second stage, the relationship between the damaged image and the relevant image is determined in terms of translation, scaling, and rotation. Ultimately, the destroyed area is reconstructed through the application of the inverse transformation. The quality assessment of inpainted images can be evaluated using metrics such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Mean Squared Error (MSE). The experimental findings provide evidence that the suggested inpainting technique is effective in terms of both speed and quality.