Multiple Image Features Research Articles

Impact of placental location matter in placenta accreta spectrum disorders.

Multiple magnetic resonance imaging (MRI) features suggestive of placenta accreta spectrum (PAS) disorders exist. However, the impact of placental location on clinical characteristics and MRI features in PAS has not been fully explored. The aim of this study was to explore the difference of MRI signs in different placental position in PAS disorders. We retrospectively reviewed surgically or pathologically confirmed PAS cases at Sichuan Provincial People's Hospital from 2016 to 2021. Placental location was categorized based on MRI as anterior, posterior, or anterior/posterior. MRI features were thoroughly reviewed and compared. A total of 262 patients were included in the study, comprising 38 (14.50%) with placenta accreta, 120 (45.80%) with placenta increta, 21 (8.02%) with placenta percreta, and 83 (31.68%) with normal placentas. The distribution of placental location was as follows: 32.06% posterior, 48.09% anterior, and 19.85% anterior/posterior. Placental location varied significantly between patients with and without PAS disorders and among patients with different PAS subtypes (P<0.05). The prevalence of placental bulge was higher in anterior and anterior/posterior placentas than in the posterior placentas (P<0.05). Moreover, T2 dark bands, placental heterogeneity, abnormal intraplacental vascularity, focal exophytic mass, and bladder wall interruption varied among different subtypes of PAS disorders (P<0.05). Placental bulge emerged as the only MRI sign that exhibited differences based on placental location. Furthermore, MRI features demonstrated variations across different subtypes of PAS.

Enhancing the differential diagnosis of small pulmonary nodules: a comprehensive model integrating plasma methylation, protein biomarkers, and LDCT imaging features

BackgroundAccurate differentiation between malignant and benign pulmonary nodules, especially those measuring 5–10 mm in diameter, continues to pose a significant diagnostic challenge. This study introduces a novel, precise approach by integrating circulating cell-free DNA (cfDNA) methylation patterns, protein profiling, and computed tomography (CT) imaging features to enhance the classification of pulmonary nodules.MethodsBlood samples were collected from 419 participants diagnosed with pulmonary nodules ranging from 5 to 30 mm in size, before any disease-altering procedures such as treatment or surgical intervention. High-throughput bisulfite sequencing was used to conduct DNA methylation profiling, while protein profiling was performed utilizing the Olink proximity extension assay. The dataset was divided into a training set and an independent test set. The training set included 162 matched cases of benign and malignant nodules, balanced for sex and age. In contrast, the test set consisted of 46 benign and 49 malignant nodules. By effectively integrating both molecular (DNA methylation and protein profiling) and CT imaging parameters, a sophisticated deep learning-based classifier was developed to accurately distinguish between benign and malignant pulmonary nodules.ResultsOur results demonstrate that the integrated model is both accurate and robust in distinguishing between benign and malignant pulmonary nodules. It achieved an AUC score 0.925 (sensitivity = 83.7%, specificity = 82.6%) in classifying test set. The performance of the integrated model was significantly higher than that of individual methylation (AUC = 0.799, P = 0.004), protein (AUC = 0.846, P = 0.009), and imaging models (AUC = 0.866, P = 0.01). Importantly, the integrated model achieved a higher AUC of 0.951 (sensitivity = 83.9%, specificity = 89.7%) in 5–10 mm small nodules. These results collectively confirm the accuracy and robustness of our model in detecting malignant nodules from benign ones.ConclusionsOur study presents a promising noninvasive approach to distinguish the malignancy of pulmonary nodules using multiple molecular and imaging features, which has the potential to assist in clinical decision-making.Trial registration: This study was registered on ClinicalTrials.gov on 01/01/2020 (NCT05432128). https://classic.clinicaltrials.gov/ct2/show/NCT05432128.

MSMTRIU-Net: Deep Learning-Based Method for Identifying Rice Cultivation Areas Using Multi-Source and Multi-Temporal Remote Sensing Images.

Identifying rice cultivation areas in a timely and accurate manner holds great significance in comprehending the overall distribution pattern of rice and formulating agricultural policies. The remote sensing observation technique provides a convenient means to monitor the distribution of rice cultivation areas on a large scale. Single-source or single-temporal remote sensing images are often used in many studies, which makes the information of rice in different types of images and different growth stages hard to be utilized, leading to unsatisfactory identification results. This paper presents a rice cultivation area identification method based on a deep learning model using multi-source and multi-temporal remote sensing images. Specifically, a U-Net based model is employed to identify the rice planting areas using both the Landsat-8 optical dataset and Sentinel-1 Polarimetric Synthetic Aperture Radar (PolSAR) dataset; to take full into account of the spectral reflectance traits and polarimetric scattering traits of rice in different periods, multiple image features from multi-temporal Landsat-8 and Sentinel-1 images are fed into the network to train the model. The experimental results on China's Sanjiang Plain demonstrate the high classification precisions of the proposed Multi-Source and Multi-Temporal Rice Identification U-Net (MSMTRIU-NET) and that inputting more information from multi-source and multi-temporal images into the network can indeed improve the classification performance; further, the classification map exhibits greater continuity, and the demarcations between rice cultivation regions and surrounding environments reflect reality more accurately.

An automatic and efficient fault diagnosis strategy for air conditioning units by combining attention mechanisms

The air conditioning system is a very important energy-consuming device in the field of construction. Timely troubleshooting of air conditioning chillers is an important aspect of reducing building energy consumption. Aiming at the problems of many model parameters, large amount of calculation and strong dependence of diagnostic performance on input features in data-driven diagnostic methods, this paper proposes an efficient fault diagnosis strategy combining RepVGG network and SENet attention mechanism. The multi-parameter feature signal is converted into a two-dimensional matrix to obtain multiple feature images. Based on the SENet attention mechanism, the multi-channel weights of the images are extracted, and the weighted feature image is input into the RepVGG network for feature depth mining and classification. Therefore, the method proposed in this paper greatly reduces the calculation amount of parameters, improves the representativeness of signal features to faults and the efficiency of model training. The effectiveness of the method is validated using the ASHRAE RP-1043 chiller unit dataset. The results show that the accuracy of this method reaches 96.55%, which is significantly better than the comparison methods PSO-ELM(96.4%), DT(95.1%), RepVGG(91.6%), ELM(87.6%) and BP(80.8%).

Role of Diffusion-Weighted Magnetic Resonance Imaging in Detection of Lymph Node Metastasis in Rectal Cancer

Abstract Background Rectal cancer is one of the most common malignancies of the digestive tract, ranking first in the worldwide incidence of malignant tumors. The early diagnosis, accurate staging and early treatment of rectal cancer are key to improve the five-year survival rate. Rectal MRI plays a crucial role in evaluating rectal cancer by providing multiple prognostic findings and imaging features that guide proper patient management. Aim of the Work to evaluate the performance of diffusion-weighted MRI (DWI) for the detection of lymph nodes and for differentiating between benign and metastatic nodes during primary rectal cancer staging. Patients and Methods the study was carried out at Ain Shams University Hospitals, Radiodiagnosis Department on 35 patients, 29 patients were histopathologically proven rectal carcinoma underwent surgical resection and 6 cases were potentially free of cancer considered as control. Results We found that the size of metastatic lymph nodes on DWI was significantly larger than that of non-metastatic lymph nodes (0.97± 0.37 vs. 0.4± 0.17 cm), most of malignant nodes had rounded shape (55.6 %) vs. oval shaped (70.6%) in benign nodes and almost all of metastatic lymph nodes had irregular margin (97.2%) while all benign nodes appeared having smooth margin. The mean ADC value was significantly lower for metastatic lymph nodes than non-metastatic lymph nodes (1.21± 0.54 vs. 1.51± 0.51 x 103 mm²/sec). In addition, for evaluation of lymph node metastasis that reflects the primary tumor characteristics, the LN/T ratio (defined as the ratio of lymph node ADC value to the primary tumor ADC value) was calculated. It was significantly lower for metastatic lymph nodes than non-metastatic lymph nodes (1.06± 0.37 vs. 1.65± 0.73). Receiver operating characteristic curve analysis revealed that the best performing cutoffs were 0.5 cm for lymph node size, 0.987 x 103 mm²/ sec for ADC value, and 0.762 for LN/T ratio. Accuracy was significantly greater for lymph nodes with irregular margin (92.3%), than LN/T ratio (78.5%) than for lymph node size (62.0%) or ADC value (74.8%). Conclusion Preoperative DWI, especially the margin of lymph nodes as well as LN/T ADC ratio, are recommended for evaluation of lymph node metastasis in patient with rectal cancer.

Feature-Selection-Based Unsupervised Transfer Learning for Change Detection from VHR Optical Images

Accurate understanding of urban land use change information is of great significance for urban planning, urban monitoring, and disaster assessment. The use of Very-High-Resolution (VHR) remote sensing images for change detection on urban land features has gradually become mainstream. However, most existing transfer learning-based change detection models compute multiple deep image features, leading to feature redundancy. Therefore, we propose a Transfer Learning Change Detection Model Based on Change Feature Selection (TL-FS). The proposed method involves using a pretrained transfer learning model framework to compute deep features from multitemporal remote sensing images. A change feature selection algorithm is then designed to filter relevant change information. Subsequently, these change features are combined into a vector. The Change Vector Analysis (CVA) is employed to calculate the magnitude of change in the vector. Finally, the Fuzzy C-Means (FCM) classification is utilized to obtain binary change detection results. In this study, we selected four VHR optical image datasets from Beijing-2 for the experiment. Compared with the Change Vector Analysis and Spectral Gradient Difference, the TL-FS method had maximum increases of 26.41% in the F1-score, 38.04% in precision, 29.88% in recall, and 26.15% in the overall accuracy. The results of the ablation experiments also indicate that TL-FS could provide clearer texture and shape detections for dual-temporal VHR image changes. It can effectively detect complex features in urban scenes.

Machine learning-assisted diagnosis of parotid tumor by using contrast-enhanced CT imaging features

PurposeThis study aims to develop a machine learning diagnostic model for parotid gland tumors based on preoperative contrast-enhanced CT imaging features to assist in clinical decision-making. Materials and methodsClinical data and contrast-enhanced CT images of 144 patients with parotid gland tumors from the Peking University School of Stomatology Hospital, collected from January 2019 to December 2022, were gathered. The 3D slicer software was utilized to accurately annotate the tumor regions, followed by exploring the correlation between multiple preoperative contrast-enhanced CT imaging features and the benign or malignant nature of the tumor, as well as the type of benign tumor. A prediction model was constructed using the k-nearest neighbors (KNN) algorithm. ResultsThrough feature selection, four key features—morphology, adjacent structure invasion, boundary, and suspicious cervical lymph node metastasis—were identified as crucial in preoperative discrimination between benign and malignant tumors. The KNN prediction model achieved an accuracy rate of 94.44 %. Additionally, six features including arterial phase CT value, age, delayed phase CT value, pre-contrast CT value, venous phase CT value, and gender, were also significant in the classification of benign tumors, with a KNN prediction model accuracy of 95.24 %. ConclusionThe machine learning model based on preoperative contrast-enhanced CT imaging features can effectively discriminate between benign and malignant parotid gland tumors and classify benign tumors, providing valuable reference information for clinicians.

An imaging-based model to predict the malignant potential of intraductal papillary mucinous neoplasm of the pancreas.

To develop and validate imaging-based models for predicting the malignancy risk of intraductal papillary mucinous neoplasm (IPMN). We retrospectively analyzed data from 241 IPMN patients who underwent preoperative CT and MRI for model development. Cyst size, presence and size of the enhancing mural nodule (EMN), main pancreatic duct (MPD) diameter, thickened/enhancing cyst wall, abrupt MPD caliber change with distal atrophy, and lymphadenopathy were assessed. Multiple logistic regression models predicting malignancy risk were created using either continuous (Model C) or dichotomized variables (Model D) using these imaging features. Validation included internal (n = 55) and external (n = 43) datasets. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) and compared with that of the international guideline-based model (Model F). Model C identified age, EMN size, MPD diameter, and lymphadenopathy as independent predictors on CT, and age and presence and size of EMN on MRI. Model D identified age ≥ 68, cyst size ≥ 31 mm, EMN ≥ 6 mm, MPD ≥ 7 mm, and lymphadenopathy as independent predictors on CT, and age ≥ 68, EMN ≥ 4.5 mm, and lymphadenopathy on MRI. Model C (AUC, 0.763-0.899) performed slightly better than Model D (AUC, 0.753-0.912) without statistical significance. No significant difference was observed between Models C and F (AUC, 0.729-0.952). Combining Model C with obstructive jaundice improved performance (AUC, 0.802-0.941) without statistical significance. Our imaging-based models effectively predicted the malignancy risk of IPMNs, comparable to international consensus guidelines. Imaging features are important for predicting the malignant potential of IPMNs. Our imaging-based model may help determine surgical candidacy for patients with IPMNs. Non-invasively determining the malignant potential of intraductal papillary mucinous neoplasms (IPMNs) allows for appropriate treatment decision-making We identified multiple imaging features that are associated with malignant transformation and developed models for this prediction. Our model performs comparably with international consensus guidelines in predicting the malignant potential of IPMNs.

Radiogenomics of Intrahepatic Cholangiocarcinoma: Correlation of Imaging Features With BAP1 and FGFR Molecular Subtypes.

Clinical research has shown unique tumor behavioral characteristics of BRCA-associated protein-1- (BAP1-) and fibroblast growth factor receptor (FGFR)-mutated intrahepatic cholangiocarcinomas (CCAs), with BAP1-mutated tumors demonstrating more aggressive forms of disease and FGFR-altered CCAs showing more indolent behavior. We performed a retrospective case-control study to evaluate for unique imaging features associated with BAP1 and FGFR genomic markers in intrahepatic CCA (iCCA). Multiple imaging features of iCCA at first staging were analyzed by 2 abdominal radiologists blinded to genomic data. Growth and development of metastases at available follow-up imaging were also recorded, as were basic clinical cohort data. Types of iCCA analyzed included those with BAP1, FGFR, or both alterations, as well as cases with low mutational burden or mutations with low clinical impact, which served as a control or "wild-type" group. There were 18 cases in the FGFR group, 10 with BAP1 mutations, and 31 wild types (controls). Cases with BAP1 mutations showed significantly larger growth at first year of follow-up (P = 0.03) and more frequent tumor-associated biliary ductal dilatation (P = 0.04) compared with controls. FGFR-altered cases showed more infiltrative margins compared with controls (P = 0.047) and demonstrated less enhancement between arterial to portal venous phases (P = 0.02). BAP1 and FGFR groups had more cases with stage IV disease at presentation than controls (P = 0.025, P = 0.006). Compared with wild-type iCCAs, FGFR-mutated tumors often demonstrate infiltrative margins, and BAP1 tumors show increased biliary ductal dilatation at presentation. BAP1-mutated cases had significantly larger growth at first-year restaging.

LPI radar waveform recognition model based on multiple feature image and quasi-residual attention module

Abstract Detecting and classifying the modulation type of the intercepted noisy LPI radar waveform has become a hot topic in the field of Electronic Countermeasures (ECM). In this paper, we propose a recognition model based on multiple feature images (MFI) and convolutional neural networks (CNN) with the quasi-residual attention module (QRAM). The core technologies of this model are divided into two parts. One is the MFI that combines the ambiguity function image (AFI) of the complete signal, covariance matrix image (CMI) of the reconstructed signal, and short-time Fourier transform image (STFTI) of the truncated signal into RGB color image in signal processing as the input for recognition network. The other is CNN with a QRAM recognition network, in which the input is sliced into two mappings called identity mapping and residual mapping to build the quasi-residual module, and the attention module is embedded in each mapping to denoise and enhance the feature. The performance of the MFI-QRAM model is demonstrated by recognizing 12 modulation types of the signals defined in this paper. The simulation experiments show that the model has strong robustness with the number of train sets and the variation of SNR. The overall probability of successful recognition (PSR) is 96.17% when the SNR is −6 dB.

Automated breast imaging report generation based on the integration of multiple image features in a metadata format for shared decision-making

Importance: Medical imaging increases the workload involved in writing reports. Given the lack of a standardized format for reports, reports are not easily used as communication tools. Objective: During medical team–patient communication, the descriptions in reports also need to be understood. Automatically generated imaging reports with rich and understandable information can improve medical quality. Design, setting, and participants: The image analysis theory of Panofsky and Shatford from the perspective of image metadata was used in this study to establish a medical image interpretation template (MIIT) for automated image report generation. Main outcomes and measures: The image information included digital imaging and communications in medicine (DICOM), reporting and data systems (RADSs), and image features used in computer-aided diagnosis (CAD). The utility of the images was evaluated by a questionnaire survey to determine whether the image content could be better understood. Results: In 100 responses, exploratory factor analysis revealed that the factor loadings of the facets were greater than 0.5, indicating construct validity, and the overall Cronbach’s alpha was 0.916, indicating reliability. No significant differences were noted according to sex, age or education. Conclusions and relevance: Overall, the results show that MIIT is helpful for understanding the content of medical images.

A comprehensive evaluation of imaging features in pediatric spinal gliomas and their value in predicting tumor grade and histology

PurposePediatric spinal cord gliomas (PSGs) are rare in children and few reports detail their imaging features. We tested the association of tumoral grade with imaging features and proposed a novel approach to categorize post-contrast enhancement patterns in PSGs.MethodsThis single-center, retrospective study included patients <21 years of age with preoperative spinal MRI and confirmed pathological diagnosis of PSG from 2000-2022. Tumors were classified using the 5th edition of the WHO CNS Tumors Classification. Two radiologists reviewed multiple imaging features, and classified enhancement patterns using a novel approach. Fisher's exact test determined associations between imaging and histological features.ResultsForty-one PSGs were reviewed. Thirty-four were intramedullary, and seven were extramedullary. Pilocytic astrocytoma was the most common tumor (39.02%). Pain and weakness were the most prevalent symptoms. Seven patients (17.07%) died. Cyst, syringomyelia, and leptomeningeal enhancement were associated with tumor grade. Widening of the spinal canal was observed only in low-grade astrocytomas. There was a significant association between tumor grade and contrast enhancement pattern. Specifically, low-grade PSGs were more likely to exhibit type 1A enhancement (mass-like, with well-defined enhancing margins) and less likely to exhibit type 1B enhancement (mass-like, with ill-defined enhancing margins).ConclusionPSGs display overlapping imaging features, making grade differentiation challenging based solely on imaging. The correlation between tumor grade and contrast enhancement patterns suggests a potential diagnostic avenue, requiring further validation with larger, multicenter studies. Furthermore, Low-grade PSGs display cysts and syringomyelia more frequently, and leptomeningeal enhancement is less common.

GAN-MAT: Generative adversarial network-based microstructural profile covariance analysis toolbox

Multimodal magnetic resonance imaging (MRI) provides complementary information for investigating brain structure and function; for example, an in vivo microstructure-sensitive proxy can be estimated using the ratio between T1- and T2-weighted structural MRI. However, acquiring multiple imaging modalities is challenging in patients with inattentive disorders. In this study, we proposed a comprehensive framework to provide multiple imaging features related to the brain microstructure using only T1-weighted MRI. Our toolbox consists of (i) synthesizing T2-weighted MRI from T1-weighted MRI using a conditional generative adversarial network; (ii) estimating microstructural features, including intracortical covariance and moment features of cortical layer-wise microstructural profiles; and (iii) generating a microstructural gradient, which is a low-dimensional representation of the intracortical microstructure profile. We trained and tested our toolbox using T1- and T2-weighted MRI scans of 1,104 healthy young adults obtained from the Human Connectome Project database. We found that the synthesized T2-weighted MRI was very similar to the actual image and that the synthesized data successfully reproduced the microstructural features. The toolbox was validated using an independent dataset containing healthy controls and patients with episodic migraine as well as the atypical developmental condition of autism spectrum disorder. Our toolbox may provide a new paradigm for analyzing multimodal structural MRI in the neuroscience community and is openly accessible at https://github.com/CAMIN-neuro/GAN-MAT.

Automatic measurement of slug flow processes from in‐line videos

AbstractSlug flow processes draw much attention in chemical and pharmaceutical manufacturing thanks to their ability to eliminate downtime costs and batch‐to‐batch variation. Using in‐line video can monitor the current process status and improve the stability of the slug flow. However, there are no effective image processing methods aiming at such videos from chemical experiments due to limited training sample size and the variety of experimental settings. The paper proposes a training‐free method to automatically detect and measure the slugs from in‐line videos. Multiple image features are fused to identify the slug shapes under various lighting conditions, a six‐point model is fitted to achieve better volume estimation, and a consistency score is estimated to quantify the detection uncertainty. The proposed algorithm achieves similar results to manual labeling for various types of slug flow processes, improving the accuracy of slug column estimation by a large margin compared to the existing automatic methods. Finally, using it to monitor the slug flow process's stability and the change of the slug volume in an injection point are demonstrated. The method not only shows that handcrafted image features have the capability for detection and segmentation from chemical experiment images but also paves the road for a training‐based algorithm for the task.

A proposed inclusion of magnetic resonance imaging features to the VI RADS to enhance its accuracy in predicting muscle invasion

BackgroundMuscle invasion in bladder cancer is a paramount factor in prognosis and setting the management plan. MRI is gaining preference in this field, being noninvasive with no radiation hazards and having good resolution, especially with the development of the standardized system of (VI RADS). Moreover, multiple other imaging features can aid in predicting muscle invasion. We studied some of the most commonly reported features to develop the most reliable combination to anticipate the presence of muscle invasion.ResultsOur prospective study on 80 patients showed 39 (48.75%) muscle invasive (MIBC) and 41 (51.25%) non-muscle invasive (NMIBC) bladder cancer cases. The inter-observer agreement on the VI RADS score and the ADC measurements were very good and they had high-accuracy predicting muscle invasion with areas under the curve (AUCs) on ROC curve analysis reaching 0.905 and 0.857, respectively. The imaging variables that showed statistically significant differences between NMIBC and MIBC cases were: the multiplicity of the lesions, vesicoureteric junction (VUJ) involvement with distal ureteric backpressure, tumor–wall contact length (TCL), tumor volume, tumor shape (sessile or papillary), presence of a stalk, the final VI RADS score and the ADC value. On the multiple regression analysis model, the multiplicity of the lesions, the minimum ADC value by ROI method and the final VI RADS score showed independent correlation with muscle invasion, negatively with the first two and positively with the latter. The combination of the six statistically significant variables on the univariate regression analysis (final VI RADS score, minimum ADC by ROI, multiplicity, index tumor shape, TCL and distal ureteric backpressure changes) showed the best AUC (0.944).ConclusionsVI RADS has good diagnostic accuracy regarding muscle invasion; however, this can even be enhanced by including other quantitative and qualitative commonly reported MRI features as a proposed modification to the VI RADS.

Machine learning assisted feature identification and prediction of hemodynamic endpoints using computed tomography in patients with CTEPH.

Chronic thromboembolic pulmonary hypertension (CTEPH) is a rare but potentially curable cause of pulmonary hypertension (PH). Currently PH is diagnosed by right heart catheterisation. Computed tomography (CT) is used for ruling out other causes and operative planning. This study aims to evaluate importance of different quantitative/qualitative imaging features and develop a supervised machine learning (ML) model to predict hemodynamic risk groups. 127 Patients with diagnosed CTEPH who received preoperative right heart catheterization and thoracic CTA examinations (39 ECG-gated; 88 non-ECG gated) were included. 19 qualitative/quantitative imaging features and 3 hemodynamic parameters [mean pulmonary artery pressure, right atrial pressure (RAP), pulmonary artery oxygen saturation (PA SaO2)] were gathered. Diameter-based CT features were measured in axial and adjusted multiplane reconstructions (MPR). Univariate analysis was performed for qualitative and quantitative features. A random forest algorithm was trained on imaging features to predict hemodynamic risk groups. Feature importance was calculated for all models. Qualitative and quantitative parameters showed no significant differences between ECG and non-ECG gated CTs. Depending on reconstruction plane, five quantitative features were significantly different, but mean absolute difference between parameters (MPR vs. axial) was 0.3mm with no difference in correlation with hemodynamic parameters. Univariate analysis showed moderate to strong correlation for multiple imaging features with hemodynamic parameters. The model achieved an AUC score of 0.82 for the mPAP based risk stratification and 0.74 for the PA SaO2 risk stratification. Contrast agent retention in hepatic vein, mosaic attenuation pattern and the ratio right atrium/left ventricle were the most important features among other parameters. Quantitative and qualitative imaging features of reconstructions correlate with hemodynamic parameters in preoperative CTEPH patients-regardless of MPR adaption. Machine learning based analysis of preoperative imaging features can be used for non-invasive risk stratification. Qualitative features seem to be more important than previously anticipated.

CFANet: Context Feature Fusion and Attention Mechanism Based Network for Small Target Segmentation in Medical Images.

Medical image segmentation plays a crucial role in clinical diagnosis, treatment planning, and disease monitoring. The automatic segmentation method based on deep learning has developed rapidly, with segmentation results comparable to clinical experts for large objects, but the segmentation accuracy for small objects is still unsatisfactory. Current segmentation methods based on deep learning find it difficult to extract multiple scale features of medical images, leading to an insufficient detection capability for smaller objects. In this paper, we propose a context feature fusion and attention mechanism based network for small target segmentation in medical images called CFANet. CFANet is based on U-Net structure, including the encoder and the decoder, and incorporates two key modules, context feature fusion (CFF) and effective channel spatial attention (ECSA), in order to improve segmentation performance. The CFF module utilizes contextual information from different scales to enhance the representation of small targets. By fusing multi-scale features, the network captures local and global contextual cues, which are critical for accurate segmentation. The ECSA module further enhances the network's ability to capture long-range dependencies by incorporating attention mechanisms at the spatial and channel levels, which allows the network to focus on information-rich regions while suppressing irrelevant or noisy features. Extensive experiments are conducted on four challenging medical image datasets, namely ADAM, LUNA16, Thoracic OAR, and WORD. Experimental results show that CFANet outperforms state-of-the-art methods in terms of segmentation accuracy and robustness. The proposed method achieves excellent performance in segmenting small targets in medical images, demonstrating its potential in various clinical applications.

Deep neural networks for multimodal perception and human-computer interaction technology in art design

Abstract The first part of this paper examines the aesthetic and application advantages of art design using human-computer interaction technology and develops a multimodal perceptual human-computer interaction system for art design. Multimodal data is obtained using multi-scale convolutional kernels for acoustic feature extraction and deep convolutional neural networks for multiple interaction image feature fusion. Finally, a test analysis is conducted to verify the system's effectiveness in this paper. According to the results, the system has an average wake-up success rate of 99.51% and a wake-up response time of 0.3665 seconds. Implementing human-computer interaction technology and deep neural networks in art design is effective and promotes the development of art design.

Application of the Cytokinesis-Block Micronucleus Assay for High-Dose Exposures Using Imaging Flow Cytometry

The cytokinesis-block micronucleus assay is a well-established method to assess radiation-induced genetic damage in human cells. This assay has been adapted to imaging flow cytometry (IFC), allowing automated analysis of many cells, and eliminating the need to create microscope slides. Furthermore, to improve the efficiency of assay performance, a small-volume method previously developed was employed. Irradiated human blood samples were cultured, stained, and analyzed by IFC to produce images of the cells. Samples were run using both manual and 96-well plate automated acquisition. Multiple parameter-based image features were collected for each sample, and the results were compared to confirm that these acquisition methods are functionally identical. This paper details the multi-parametric analysis developed and the resulting calibration curves up to 10 Gy. The calibration curves were created using a quadratic random coefficient model with Poisson errors, as well as a logistic discriminant function. The curves were then validated with blinded, irradiated samples, using relative bias and relative mean square error. Overall, the accuracy of the dose estimates was adequate for triage dosimetry (within 1 Gy of the true dose) over 90% of the time for lower doses and about half the time for higher doses, with the lowest success rate between 5 and 6 Gy where the calibration curve reached its peak and there was the smallest change in MN/BNC with dose. This work describes the application of a novel multi-parametric analysis that fits the calibration curves and allows dose estimates up to 10 Gy, which were previously limited to 4 Gy. Furthermore, it demonstrates that the results from samples acquired manually and with the autosampler are functionally similar.

Multi-Difference Image Fusion Change Detection Using a Visual Attention Model on VHR Satellite Data

For very-high-resolution (VHR) remote sensing images with complex objects and rich textural information, multi-difference image fusion has been proven as an effective method to improve the performance of change detection. However, errors are superimposed during this process and a single spectral feature cannot fully utilize the correlation between pixels, resulting in low robustness. To overcome these problems and optimize the performance of multi-difference image fusion in change detection, we propose a novel multi-difference image fusion change detection method based on a visual attention model (VA-MDCD). First, we construct difference images using change vector analysis (CVA) and spectral gradient difference (SGD). Second, we use the visual attention model to calculate multiple color, intensity and orientation features of the difference images to obtain the difference saliency images. Third, we use the wavelet transform fusion algorithm to fuse two saliency images. Finally, we execute the OTSU threshold segmentation algorithm (OTSU) to obtain the final change detection map. To validate the effectiveness of VA-MDCD on VHR images, two datasets of Jilin 1 and Beijing 2 are selected for experiments. Compared with classical methods, the proposed method has a better performance with fewer missed alarms (MA) and false alarms (FA), which proves that the method has a strong robustness and generalization ability. The F-measure of the two datasets is 0.6671 and 0.7313, respectively. In addition, the results of ablation experiments confirm that the three feature extraction modules of the model all play a positive role.