Segmentation Feature Extraction Research Articles

Metastatic Lung Adenocarcinomas: Development and Evaluation of Radiomic-Based Methods to Measure Baseline Intra-Patient Inter-Tumor Lesion Heterogeneity.

Radiomics has traditionally focused on individual tumors, often neglecting the integration of metastatic disease, particularly in patients with non-small cell lung cancer. This study sought to examine intra-patient inter-tumor lesion heterogeneity indices using radiomics, exploring their relevance in metastatic lung adenocarcinoma. Consecutive adults newly diagnosed with metastatic lung adenocarcinoma underwent contrast-enhanced CT scans for lesion segmentation and radiomic feature extraction. Three methods were devised to measure distances between tumor lesion profiles within the same patient in radiomic space: centroid to lesion, lesion to lesion, and primitive to lesion, with subsequent calculation of mean, range, and standard deviation of these distances. Associations between HIs, disease control rate, objective response rate to first-line treatment, and overall survival were explored. The study included 167 patients (median age 62.3years) between 2016 and 2019, divided randomly into experimental (N = 117,546 lesions) and validation (N = 50,232 tumor lesions) cohorts. Patients without disease control/objective response and with poorer survival consistently systematically exhibited values of all heterogeneity indices. Multivariable analyses revealed that the range of primitive-to-lesion distances was associated with disease control in both cohorts and with objective response in the validation cohort. This metrics showed univariable associations with overall survival in the experimental. In conclusion, we proposed original methods to estimate the intra-patient inter-tumor lesion heterogeneity using radiomics that demonstrated correlations with patient outcomes, shedding light on the clinical implications of inter-metastases heterogeneity. This underscores the potential of radiomics in understanding and potentially predicting treatment response and prognosis in metastatic lung adenocarcinoma patients.

Detecting tumors in medical images using segmentation and feature extraction techniques

Tumors rank among the most prevalent and lethal diseases worldwide, emphasizing the paramount importance of early detection in mitigating mortality rates. There has been a tremendous growth in medical image segmentation and classification for diagnosing cancer. This led to the various developments in medical research that help in diagnosing types of cancers and other human medical problems. This work focuses on performing effective segmentation procedures and feature extraction by a proposed hybrid intelligent technique for detecting tumors in the medical images. Additionally, the research addresses the design aspects of feature extraction, image classification, and texture features. These features are reduced by employing feature selection. Finally, experiments were conducted to compare tumor detection in medical images using the proposed algorithm with other existing algorithms. The results demonstrated promising achievements in terms of their evaluation metrics.

Leveraging Deep Learning for Automated Reconstruction of Indoor Unstructured Elements in Scan-to-BIM

Abstract. Achieving automatic 3D reconstruction for indoor scenes is extremely useful in the field of scene understanding. Building information modeling (BIM) models are essential for lowering project costs, assisting in building planning and renovations, as well as improving building management efficiency. However, nearly all current available scan-to-BIM approaches employ manual or semi-automatic methods. These approaches concentrate solely on significant structured objects, neglecting other unstructured elements such as furniture. The limitation arises from challenges in modeling incomplete point clouds of obstructed objects and capturing indoor scene details. Therefore, this research introduces an innovative and effective reconstruction framework based on deep learning semantic segmentation and model-driven techniques to address these limitations. The proposed framework utilizes wall segment recognition, feature extraction, opening detection, and automatic modeling to reconstruct 3D structured models of point clouds with different room layouts in both Manhattan and non-Manhattan architectures. Moreover, it provides 3D BIM models of actual unstructured elements by detecting objects, completing point clouds, establishing bounding boxes, determining type and orientation, and automatically generating 3D BIM models with a parametric algorithm implemented into the Revit software. We evaluated this framework using publicly available and locally generated point cloud datasets with varying furniture combinations and layout complexity. The results demonstrate the proposed framework's efficiency in reconstructing structured indoor elements, exhibiting completeness and geometric accuracy, and achieving precision and recall values greater than 98%. Furthermore, the generated unstructured 3D BIM models keep essential real-scene characteristics such as geometry, spatial locations, numerical aspects, various shapes, and orientations compared to literature methods.

Cyclone Intensity Estimation System using Satellite Images

Tropical cyclones are big storms that can cause a lot of damage. Cyclone intensity estimation plays a vital role in disaster preparedness, response, and mitigation strategies. This paper introduces a novel approach to estimating cyclone intensity using satellite images through a Convolutional Neural Network (CNN) model. Unlike previous methods, we employ advanced techniques such as histogram analysis for feature extraction and adaptive thresholding for image segmentation using mean, Gaussian, and Otsu methods. The model also predicts potential coverage distance. Additionally, we present a user-friendly visualization portal, a pioneering effort in this field, which displays the deep learning output along with contextual information for end-users

Damage Detection with Data-Driven Machine Learning Models on an Experimental Structure

Various techniques have been employed to detect damage in civil engineering structures. Apart from the model-based approach, which demands the frequent updating of its corresponding finite element method (FEM)-built model, data-driven methods have gained prominence. Environmental and operational effects significantly affect damage detection due to the presence of damage-related trends in their analyses. Time-domain approaches such as autoregression and metrics such as the Mahalanobis squared distance have been utilized to mitigate these effects. In the realm of machine learning (ML) models, their effectiveness relies heavily on the type and quality of the extracted features, making this aspect a focal point of attention. The objective of this work is therefore to deploy and observe potential feature extraction approaches used as input in training fully data-driven damage detection machine learning models. The most damage-sensitive segment (MDSS) feature extraction technique, which potentially treats signals under multiple conditions, is also proposed and deployed. It identifies potential segments for each feature coefficient under a defined criterion. Therefore, 680 signals, each consisting of 8192 data points, are recorded using accelerometer sensors at the Los Alamos National Laboratory in the USA. The data are obtained from a three-story 3D building frame and are utilized in this research for a mainly data-driven damage detection task. Three approaches are implemented to replace four missing signals with the generated ones. In this paper, multiple fast Fourier and wavelet-transformed features are employed to evaluate their performance. Most importantly, a power spectral density (PSD)-based feature extraction approach that considers the maximum variability criterion to identify the most sensitive segments is developed and implemented. The performance of the MDSS selection technique, proposed in this work, surpasses that of all 18 trained neural networks (NN) and recurrent neural network (RNN) models, achieving more than 80% prediction accuracy on an unseen prediction dataset. It also significantly reduces the feature dimension. Furthermore, a sensitivity analysis is conducted on signal segmentation, overlapping, the treatment of a training dataset imbalance, and principal component analysis (PCA) implementation across various combinations of features. Binary and multiclass classification models are employed to primarily detect and additionally locate and identify the severity class of the damage. The collaborative approach of feature extraction and machine learning models effectively addresses the impact of environmental and operational effects (EOFs), suppressing their influences on the damage detection process.

Securing Social Media Imagery: GAN-Driven Encryption and CNN Analysis with DEA Protection

Generative adversarial networks (GANs) transform low-dimensional random noise into a photorealistic picture. The use of such misleading pictures laden with irrelevant information on social media platforms might lead to serious and challenging issues. The goal of this study is to create and identify the affected GAN pictures. Additionally, enhanced GAN detection accuracy by preprocessing and segmentation feature extraction. This study examines the effectiveness of several learning-based methods for detecting image-to-image translation. Generative adversarial networks use deep learning approaches like convolutional neural networks for generative modelling and the DEA (Data encryption standard) is used to produce the encryption key. To accurately identify fake images, an effective picture forgery detector is necessary. Recent advancements in generative adversarial networks (GANs) have been focused on producing photorealistic pictures quickly and successfully. However, GANs can complicate visual forensics and model attribution. Data from one source and pictures from another can have a wide range of uses, including in fields like computer vision, video, and language processing. The investigation of some photos reveals that both conventional and deep learning detectors may reach up to 95% detection accuracy. However, only deep learning maintains good accuracy on compressed data. An article that explains how to spot GAN-generated false images on social media also provides context on GANs and the theoretical concepts underlying them.

PHE-SICH-CT-IDS: A benchmark CT image dataset for evaluation semantic segmentation, object detection and radiomic feature extraction of perihematomal edema in spontaneous intracerebral hemorrhage

Background and objective:Intracerebral hemorrhage is one of the diseases with the highest mortality and poorest prognosis worldwide. Spontaneous intracerebral hemorrhage (SICH) typically presents acutely, prompt and expedited radiological examination is crucial for diagnosis, localization, and quantification of the hemorrhage. Early detection and accurate segmentation of perihematomal edema (PHE) play a critical role in guiding appropriate clinical intervention and enhancing patient prognosis. However, the progress and assessment of computer-aided diagnostic methods for PHE segmentation and detection face challenges due to the scarcity of publicly accessible brain CT image datasets. Methods:This study establishes a publicly available CT dataset named PHE-SICH-CT-IDS for perihematomal edema in spontaneous intracerebral hemorrhage. The dataset comprises 120 brain CT scans and 7,022 CT images, along with corresponding medical information of the patients. To demonstrate its effectiveness, classical algorithms for semantic segmentation, object detection, and radiomic feature extraction are evaluated. The experimental results confirm the suitability of PHE-SICH-CT-IDS for assessing the performance of segmentation, detection and radiomic feature extraction methods. Results:This study conducts numerous experiments using classical machine learning and deep learning methods, demonstrating the differences in various segmentation and detection methods on the PHE-SICH-CT-IDS. The highest precision achieved in semantic segmentation is 76.31%, while object detection attains a maximum precision of 97.62%. The experimental results on radiomic feature extraction and analysis prove the suitability of PHE-SICH-CT-IDS for evaluating image features and highlight the predictive value of these features for the prognosis of SICH patients. Conclusion:To the best of our knowledge, this is the first publicly available dataset for PHE in SICH, comprising various data formats suitable for applications across diverse medical scenarios. We believe that PHE-SICH-CT-IDS will allure researchers to explore novel algorithms, providing valuable support for clinicians and patients in the clinical setting. PHE-SICH-CT-IDS is freely published for non-commercial purpose at https://figshare.com/articles/dataset/PHE-SICH-CT-IDS/23957937.

MEF-UNet: An end-to-end ultrasound image segmentation algorithm based on multi-scale feature extraction and fusion

Ultrasound image segmentation is a challenging task due to the complexity of lesion types, fuzzy boundaries, and low-contrast images along with the presence of noises and artifacts. To address these issues, we propose an end-to-end multi-scale feature extraction and fusion network (MEF-UNet) for the automatic segmentation of ultrasound images. Specifically, we first design a selective feature extraction encoder, including detail extraction stage and structure extraction stage, to precisely capture the edge details and overall shape features of the lesions. In order to enhance the representation capacity of contextual information, we develop a context information storage module in the skip-connection section, responsible for integrating information from adjacent two-layer feature maps. In addition, we design a multi-scale feature fusion module in the decoder section to merge feature maps with different scales. Experimental results indicate that our MEF-UNet can significantly improve the segmentation results in both quantitative analysis and visual effects.

Adaptive Tensor-Based Feature Extraction for Pupil Segmentation in Cataract Surgery.

Cataract surgery remains the only definitive treatment for visually significant cataracts, which are a major cause of preventable blindness worldwide. Successful performance of cataract surgery relies on stable dilation of the pupil. Automated pupil segmentation from surgical videos can assist surgeons in detecting risk factors for pupillary instability prior to the development of surgical complications. However, surgical illumination variations, surgical instrument obstruction, and lens material hydration during cataract surgery can limit pupil segmentation accuracy. To address these problems, we propose a novel method named adaptive wavelet tensor feature extraction (AWTFE). AWTFE is designed to enhance the accuracy of deep learning-powered pupil recognition systems. First, we represent the correlations among spatial information, color channels, and wavelet subbands by constructing a third-order tensor. We then utilize higher-order singular value decomposition to eliminate redundant information adaptively and estimate pupil feature information. We evaluated the proposed method by conducting experiments with state-of-the-art deep learning segmentation models on our BigCat dataset consisting of 5,700 annotated intraoperative images from 190 cataract surgeries and a public CaDIS dataset. The experimental results reveal that the AWTFE method effectively identifies features relevant to the pupil region and improved the overall performance of segmentation models by up to 2.26% (BigCat) and 3.31% (CaDIS). Incorporation of the AWTFE method led to statistically significant improvements in segmentation performance (P < 1.29 × 10-10 for each model) and yielded the highest-performing model overall (Dice coefficients of 94.74% and 96.71% for the BigCat and CaDIS datasets, respectively). In performance comparisons, the AWTFE consistently outperformed other feature extraction methods in enhancing model performance. In addition, the proposed AWTFE method significantly improved pupil recognition performance by up to 2.87% in particularly challenging phases of cataract surgery.

ECG in Real World Scenario: Time Variability in Biometric Using Wearable Smart Textile Shirts

Biomedical signals, such as an electrocardiogram (ECG), have been included in wearable platforms for biometric reasons due to the rapid expansion of apps and technology capable of gathering this physiological data. Most studies using ECG biometrics are conducted in clinical settings, which is impractical for wearable ECG-based biometric applications. Therefore, this study aims to determine the reliability of ECG signals obtained from the commercially available Hexoskin Proshirt and HeartIn Fit shirt, which may be worn for biometric verification in real-world scenarios. ECG data from 22 participants were collected over a span period of more than 30 days. The raw ECG signal is first pre-processed in the time domain using noise-removal Butterworth filters, and then a successful QRS segmented feature extraction method is used. Not to mention, 300 datasets were used to test the recommended recognition method using a Quadratic Support Vector Machine (QSVM). In comparison, around 854 datasets were prepared for training and validation of the classifier. The findings showed that the proposed method provided a considerable accuracy above 99.63 % with a FAR of 0.14 %, an FRR of 2.86 %, and a TPR of 97.14 %. Thus, the study supports using ECG biometrics for verification in real-world settings by employing a smart textile shirt with varying temporal variability.

Optimized OTSU Segmentation Algorithm-Based Temperature Feature Extraction Method for Infrared Images of Electrical Equipment.

Infrared image processing is an effective method for diagnosing faults in electrical equipment, in which target device segmentation and temperature feature extraction are key steps. Target device segmentation separates the device to be diagnosed from the image, while temperature feature extraction analyzes whether the device is overheating and has potential faults. However, the segmentation of infrared images of electrical equipment is slow due to issues such as high computational complexity, and the temperature information extracted lacks accuracy due to the insufficient consideration of the non-linear relationship between the image grayscale and temperature. Therefore, in this study, we propose an optimized maximum between-class variance thresholding method (OTSU) segmentation algorithm based on the Gray Wolf Optimization (GWO) algorithm, which accelerates the segmentation speed by optimizing the threshold determination process using OTSU. The experimental results show that compared to the non-optimized method, the optimized segmentation method increases the threshold calculation time by more than 83.99% while maintaining similar segmentation results. Based on this, to address the issue of insufficient accuracy in temperature feature extraction, we propose a temperature value extraction method for infrared images based on the K-nearest neighbor (KNN) algorithm. The experimental results demonstrate that compared to traditional linear methods, this method achieves a 73.68% improvement in the maximum residual absolute value of the extracted temperature values and a 78.95% improvement in the average residual absolute value.

Automated MRI liver segmentation for anatomical segmentation, liver volumetry, and the extraction of radiomics.

To develop and evaluate a deep convolutional neural network (DCNN) for automated liver segmentation, volumetry, and radiomic feature extraction on contrast-enhanced portal venous phase magnetic resonance imaging (MRI). This retrospective study included hepatocellular carcinoma patients from an institutional database with portal venous MRI. After manual segmentation, the data was randomly split into independent training, validation, and internal testing sets. From a collaborating institution, de-identified scans were used for external testing. The public LiverHccSeg dataset was used for further external validation. A 3D DCNN was trained to automatically segment the liver. Segmentation accuracy was quantified by theDice similarity coefficient (DSC) with respect to manual segmentation. A Mann-Whitney U test was used to compare the internal and external test sets. Agreement of volumetry and radiomic features was assessed using the intraclass correlation coefficient (ICC). In total, 470 patients met the inclusion criteria (63.9±8.2 years; 376 males) and 20 patients were used for external validation (41±12 years; 13 males). DSC segmentation accuracy of the DCNN was similarly high between the internal (0.97±0.01) and external (0.96±0.03) test sets (p=0.28) and demonstrated robust segmentation performance on public testing (0.93±0.03). Agreement of liver volumetry was satisfactory in the internal (ICC, 0.99), external (ICC, 0.97), and public (ICC, 0.85) test sets. Radiomic features demonstrated excellent agreement in the internal (mean ICC, 0.98±0.04), external (mean ICC, 0.94±0.10), and public (mean ICC, 0.91±0.09) datasets. Automated liver segmentation yields robust and generalizable segmentation performance on MRI data and can be used for volumetry and radiomic feature extraction. Liver volumetry, anatomic localization, and extraction of quantitative imaging biomarkers require accurate segmentation, but manual segmentation is time-consuming. A deep convolutional neural network demonstrates fast and accurate segmentation performance on T1-weighted portal venous MRI. • This deep convolutional neural network yields robust and generalizable liver segmentation performance on internal, external, and public testing data. • Automated liver volumetry demonstrated excellent agreement with manual volumetry. • Automated liver segmentations can be used for robust and reproducible radiomic feature extraction.

Application of Segmented Transformer Feature Extraction in Near Infrared Spectral Data Classification

Application of Segmented Transformer Feature Extraction in Near Infrared Spectral Data Classification

PAHPhysRAD: A Digital Imaging and Communications in Medicine Research Tool for Segmentation and Radiomic Feature Extraction.

Segmentation and analysis of organs at risks (OARs) and tumor volumes are integral concepts in the development of radiotherapy treatment plans and prediction of patients' treatment outcomes. To develop a research tool, PAHPhysRAD, that can be used to semi- and fully automate segmentation of OARs. In addition, the proposed software seeks to extract 3214 radiomic features from tumor volumes and user-specified dose-volume parameters. Developed within MATLAB, PAHPhysRAD provides a comprehensive suite of segmentation tools, including manual, semi-automatic, and automatic options. For semi-autosegmentation, meta AI's Segment Anything Model was incorporated using the bounding box methods. Autosegmentation of OARs and tumor volume are implemented through a module that enables the addition of models in Open Neural Network Exchange format. To validate the radiomic feature extraction module in PAHPhysRAD, radiomic features extracted from gross tumor volume of 15 non-small cell lung carcinoma patients were compared against the features extracted from 3D Slicer™. The dose-volume parameters extraction module was validated using the dose volume data extracted from 28 tangential field-based breast treatment planning datasets. The volume receiving ≥20 Gy (V20) for ipsilateral lung and the mean doses received by the heart and ipsilateral lung, were compared against the parameters extracted from Eclipse. The Wilcoxon signed-rank test revealed no significant difference between the majority of the radiomic features derived from PAHPhysRAD and 3D Slicer. The average mean lung and heart doses calculated in Eclipse were 5.51 ± 2.28 Gy and 1.64 ± 1.98 Gy, respectively. Similarly, the average mean lung and heart doses calculated in PAHPhysRAD were 5.45 ± 2.89 Gy and 1.67 ± 2.08 Gy, respectively. The MATLAB-based graphical user interface, PAHPhysRAD, offers a user-friendly platform for viewing and analyzing medical scans with options to extract radiomic features and dose-volume parameters. Its versatility, compatibility, and potential for further development make it an asset in medical image analysis.

A Machine Learning Perspective on Daisy and Dandelion Classification: Gaussian Naive Bayes with Sobel

This study explores the classification of Daisy and Dandelion flowers using a Gaussian Naive Bayes classifier, enhanced by Sobel segmentation and Hu moment feature extraction. The research adopted a quantitative approach, utilizing a balanced dataset of Daisy and Dandelion images. The Sobel operator was employed for image segmentation, accentuating the floral features crucial for classification. Hu moments, known for their invariance to image transformations, were extracted as features. The Gaussian Naive Bayes algorithm was then applied, with its performance evaluated through a 5-fold cross-validation process. The results exhibited moderate accuracy, with the highest recorded at 60%, and precision peaking at 62.60%. These findings indicate a reasonable level of effectiveness in distinguishing between the two species, though variations in performance metrics suggested room for improvement. The study contributes to the field of botanical image classification by demonstrating the potential of integrating image processing techniques with machine learning for flower classification. However, it also highlights the limitations of the Gaussian Naive Bayes approach in handling complex image data. Future research directions include exploring more advanced machine learning algorithms and expanding the feature set to enhance classification accuracy. The practical implications of this research extend to ecological monitoring and agricultural studies, where efficient and accurate plant classification is vital

STGA-MS: AI diagnosis model of regional wall motion abnormality based on 2D transthoracic echocardiography

Regional wall motion abnormality (RWMA) is a common manifestation of ischemic heart disease detected through echocardiography. Currently, RWMA diagnosis heavily relies on visual assessment by doctors, leading to limitations in experience-based dependence and suboptimal reproducibility among observers. Several RWMA diagnosis models were proposed, while RWMA diagnosis with more refined segments can provide more comprehensive wall motion information to better assist doctors in the diagnosis of ischemic heart disease. In this paper, we proposed the STGA-MS model which consists of three modules, the spatial-temporal grouping attention (STGA) module, the segment feature extraction module, and the multiscale downsampling module, for the diagnosis of RWMA for multiple myocardial segments. The STGA module captures global spatial and temporal information, enhancing the representation of myocardial motion characteristics. The segment feature extraction module focuses on specific segment regions, extracting relevant features. The multiscale downsampling module analyzes myocardial motion deformation across different receptive fields. Experimental results on a 2D transthoracic echocardiography dataset show that the proposed STGA-MS model achieves better performance compared to state-of-the-art models. It holds promise in improving the accuracy and reproducibility of RWMA diagnosis, assisting clinicians in diagnosing ischemic heart disease more reliably.

Evaluating Thresholding-Based Segmentation and Humoment Feature Extraction in Acute Lymphoblastic Leukemia Classification using Gaussian Naive Bayes

This study, titled "Evaluating Thresholding-Based Segmentation and HuMoment Feature Extraction in Acute Lymphoblastic Leukemia Classification using Gaussian Naive Bayes," investigates the application of image processing and machine learning techniques in the classification of Acute Lymphoblastic Leukemia (ALL). Utilizing a dataset of microscopic blood smear images, the research focuses on the efficacy of thresholding-based segmentation and Hu moment feature extraction in distinguishing between benign and malignant cases of ALL. Gaussian Naive Bayes, known for its simplicity and effectiveness, is employed as the classification algorithm. The study adopts a 5-fold cross-validation approach to evaluate the model's performance, with particular emphasis on metrics such as accuracy, precision, recall, and F1-score. Results indicate a high precision rate across all folds, averaging approximately 84.13%, while exhibiting variability in accuracy, recall, and F1-scores. These findings suggest that while the model is effective in identifying malignant cases, further refinements are necessary for improving overall accuracy and consistency. This research contributes to the field of medical image analysis by demonstrating the potential of combining simple yet efficient techniques for the automated diagnosis of hepatological diseases. It highlights the importance of integrating image processing with machine learning to enhance diagnostic accuracy in medical applications.

Optimizing Neurodegenerative Disease Classification with Canny Segmentation and Voting Classifier: An Imbalanced Dataset Study

This study explores the efficacy of a Voting Classifier, combining Logistic Regression, Random Forest, and Gaussian Naive Bayes, in the classification of neurodegenerative diseases, focusing on Alzheimer's Disease (AD), Parkinson’s Disease (PD), and control groups. Utilizing a dataset pre-processed with Canny segmentation and Hu Moments feature extraction, the research aimed to address the challenges posed by imbalanced datasets in medical image classification. The classifier's performance was evaluated through a 5-fold cross-validation approach, with metrics including accuracy, precision, recall, and F1-Score. The results revealed a consistent recall rate of approximately 46% across all folds, indicating the model's effectiveness in identifying cases of neurodegenerative diseases. However, the precision and F1-Score were notably lower, averaging around 22% and 29%, respectively, underscoring the difficulties in achieving accurate classification in imbalanced datasets. The study contributes to the understanding of machine learning applications in medical diagnostics, specifically in the challenging context of neurodegenerative disease classification. It highlights the potential of using advanced image processing techniques combined with machine learning ensembles in enhancing diagnostic accuracy. However, it also draws attention to the inherent challenges in such approaches, particularly regarding precision in imbalanced datasets. Recommendations for future research include exploring data balancing techniques, alternative feature extraction methods, and different machine learning algorithms to improve the precision and overall performance. Additionally, applying the model to a broader and more diverse dataset could provide more generalizable and robust findings. This study is significant for researchers and practitioners in medical imaging and machine learning, offering insights into the complexities and potential of automated disease classification

Assessing Bagging-meta Estimator in Imbalanced CT Kidney Disease Classification: A Focus on Sobel and Hu Moment Techniques

This study investigates the efficacy of the Bagging-meta estimator in classifying CT kidney diseases, focusing on an imbalanced dataset processed through Sobel segmentation and Hu moment feature extraction. The research utilized a quantitative approach, applying the Bagging-meta estimator to a dataset comprising CT images classified into four categories: Normal, Cyst, Tumor, and Stone. These images were preprocessed using Sobel segmentation to highlight critical structures and Hu moment feature extraction for robust classification features. The study employed a 5-fold cross-validation method to evaluate the model's performance, assessing metrics such as accuracy, precision, recall, and F1-Score. The results indicated a significant variation in the model's performance across different folds, with accuracy ranging from 49.86% to 66.17%, precision between 51.86% and 65.93%, recall from 57.95% to 64.44%, and F1-Scores spanning 48.26% to 60.74%. These findings suggest that while the Bagging-meta estimator can achieve reasonable accuracy in classifying kidney diseases from CT images, its performance is affected by the imbalanced nature of the dataset. This study contributes to the understanding of the challenges and potential of machine learning in medical imaging, particularly in the context of imbalanced datasets. It highlights the need for specialized approaches to handle such datasets and underscores the importance of preprocessing techniques in enhancing model performance. Future research directions include exploring methods to address data imbalance, investigating alternative feature extraction techniques, and testing the model on diverse datasets to enhance its generalizability and reliability in clinical settings. This research offers valuable insights into the development of automated diagnostic tools in medical imaging and advances the field of computer-aided diagnosis in nephrology.

Prediction of Hepatic Encephalopathy After Transjugular Intrahepatic Portosystemic Shunt Based on CT Radiomic Features of Visceral Adipose Tissue

To evaluate the performance and clinical utility of CT radiomic features of visceral adipose tissue (VAT) in the prediction of hepatic encephalopathy (HE) after transjugular intrahepatic portosystemic shunt (TIPS). This multi-center study was retrospectively designed. Patients with cirrhosis who underwent TIPS were recruited from January 2015 to December 2020. Pre-TIPS contrast-enhanced CT images were collected for VAT segmentation and radiomic feature extraction. Least absolute shrinkage and selection operator regression with ten-fold cross-validation was performed to reduce dimension. Logistic regression with regularization, support vector machine, and random forest were used for model construction. A total of 130 patients (90 men; mean age, 54±11 years) were finally enrolled. The cohort was split into 85 patients for the training set (58 men; mean age, 53±12 years) with 19 HE, 21 patients for the internal test set (17 men; mean age, 53±11 years) with 5 HE, and 24 patients for the external test set (15 men; mean age, 55±11 years). Ten radiomic features and C-reactive protein constituted radiomic-clinical models with the best performance. The average area under the receiver operating characteristic curve is 0.97 in the training set and 0.84 in the test sets. For a fixed sensitivity of 0.90, the specificity and negative predictive value of the model is 0.63 and 1.00, respectively; while for a fixed specificity of 0.90, the sensitivity and positive predictive value is 0.60 and 0.75, respectively. Machine learning models based on CT radiomic features extracted from VAT can predict post-TIPS HE with satisfactory performance. Our machine learning models based on CT radiomic features of visceral adipose tissue in patients with cirrhosis may assist in predicting hepatic encephalopathy after transjugular intrahepatic portosystemic shunt, indicating its potential in patient selection and clinical decision-making. Radiomics of visceral adipose tissue provide great help in predicting hepatic encephalopathy after transjugular intrahepatic portosystemic shunt. The clinical-radiomic models showed satisfactory performance with an average area under the receiver operating characteristic curve of 0.84. The model can hypothetically provide 90% sensitivity and 100% negative predictive value for guiding patients who are considering transjugular intrahepatic portosystemic shunt.