Journal of imaging informatics in medicine最新文献

Effective radiotherapy planning requires precise delineation of organs at risk (OARs), but the traditional manual method is laborious and subject to variability. This study explores using convolutional neural networks (CNNs) for automating OAR segmentation in pelvic CT images, focusing on the bladder, prostate, rectum, and femoral heads (FHs) as an efficient alternative to manual segmentation. Utilizing the Medical Open Network for AI (MONAI) framework, we implemented and compared U-Net, ResU-Net, SegResNet, and Attention U-Net models and explored different loss functions to enhance segmentation accuracy. Our study involved 240 patients for prostate segmentation and 220 patients for the other organs. The models' performance was evaluated using metrics such as the Dice similarity coefficient (DSC), Jaccard index (JI), and the 95th percentile Hausdorff distance (95thHD), benchmarking the results against expert segmentation masks. SegResNet outperformed all models, achieving DSC values of 0.951 for the bladder, 0.829 for the prostate, 0.860 for the rectum, 0.979 for the left FH, and 0.985 for the right FH (p < 0.05 vs. U-Net and ResU-Net). Attention U-Net also excelled, particularly for bladder and rectum segmentation. Experiments with loss functions on SegResNet showed that Dice loss consistently delivered optimal or equivalent performance across OARs, while DiceCE slightly enhanced prostate segmentation (DSC = 0.845, p = 0.0138). These results indicate that advanced CNNs, especially SegResNet, paired with optimized loss functions, provide a reliable, efficient alternative to manual methods, promising improved precision in radiotherapy planning.

The number of Mycobacterium avium-intracellulare complex pulmonary disease patients is increasing globally. Distinguishing Mycobacterium avium-intracellulare complex pulmonary disease from pulmonary tuberculosis is difficult due to similar manifestations and characteristics. We aimed to build and validate a machine learning model using clinical data and computed tomography features to differentiate them. This multi-centered, retrospective study included 169 patients diagnosed with Mycobacterium avium-intracellulare complex and pulmonary tuberculosis from date to date. Data were analyzed, and logistic regression, random forest, and support vector machine models were established and validated. Performance was evaluated using receiver operating characteristic and precision-recall curves. In total, 84 patients with Mycobacterium avium-intracellulare complex pulmonary disease and 85 with pulmonary tuberculosis were analyzed. Patients with Mycobacterium avium-intracellulare complex pulmonary disease were older. Hemoptysis rate, cavity number and morphology, bronchiectasis type, and distribution differed. The support vector machine model performed better. In the training set, the area under the curve was 0.960, and in the validation set it was 0.885. The precision-recall curve showed high accuracy and low recall for the support vector machine model. The support vector machine learning-based model, which integrates clinical data and computed tomography imaging features, exhibited excellent diagnostic performance and can assist in differentiating Mycobacterium avium-intracellulare complex pulmonary disease from pulmonary tuberculosis.

Accurately diagnosing various types of breast lesions is critical for assessing breast cancer risk and predicting patient outcomes, which necessitates a fine-grained classification approach. While convolutional neural networks (CNNs) are predominantly employed in fine-grained classification tasks for breast lesions, they often struggle to effectively capture and model the intricate relationships between local and global features, an aspect that is vital for achieving high classification accuracy. Additionally, Color Doppler Flow Imaging (CDFI) and Strain Elastography (SE) are two important ultrasound imaging techniques widely used in the diagnosis of breast lesions. However, their specific contributions to fine-grained classification have not been thoroughly investigated. In this paper, we introduce a Triple Morphological Feature Attention Network (TMAN) designed to enhance fine-grained classification of breast ultrasound images. The TMAN architecture comprises three key modules: Local Margin Attention (LMA), Structured Texture Attention (STA), and Fusion Attention (FA), each focused on extracting distinct morphological features. TMAN achieved an average accuracy of 74.40%, precision of 73.18%, and specificity of 96.02%, surpassing state-of-the-art methods. The findings reveal that incorporating CDFI significantly improved classification for malignant subtypes with a 10% accuracy boost, while SE had a negligible impact. These findings highlight the effectiveness of TMAN in extracting nuanced morphological features and advancing precision in breast ultrasound diagnosis. The source code is accessible at https://github.com/windywindyw/TMAN .

Echocardiogram analysis plays a crucial role in assessing and diagnosing cardiac function, providing essential data to support medical diagnoses of heart disease. A key task, accurately identifying and segmenting the left ventricle (LV) in echocardiograms, remains challenging and labor-intensive. Current automated cardiac segmentation methods often lack the necessary accuracy and reproducibility, while semi-automated or manual annotations are excessively time-consuming. To address these limitations, we propose a novel segmentation framework, semi-and self-supervised learning with dual attention (SSL-DA) for echocardiogram segmentation. We start with a temporal masking network for pre-training. This network captures valuable information, such as echocardiogram periodicity. It also provides optimized initialization parameters for LV segmentation. We then employ a semi-supervised network to automatically segment the left ventricle, enhancing the model's learning with channel and spatial attention mechanisms to capture global channel dependencies and spatial dependencies across annotations. We evaluated SSL-DA on the publicly available EchoNet-Dynamic dataset, achieving a Dice similarity coefficient of 93.34% (95% CI, 93.23-93.46%), outperforming most prior CNN-based models. To further assess the generalization ability of SSL-DA, we conducted ablation experiments on the CAMUS dataset. Experimental results confirm that SSL-DA can quickly and accurately segment the left ventricle in echocardiograms, showing its potential for robust clinical application.

Dual-energy subtraction (DES) chest X-ray images (CXRs) are often affected by motion artifacts resulting from patients' voluntary or involuntary movements, even in clinical settings. Additionally, the mediastinum and upper abdominal regions in low-energy (LE) CXRs are susceptible to signal insufficiency due to inadequate input photon numbers. Current image processing techniques for removing motion artifacts and statistical noise from DES-CXRs are insufficient, and potential algorithms for these tasks remain largely unexplored. We propose a framework based on paired cycle-consistency adversarial generative networks to effectively remove motion artifacts and statistical noise from DES-CXRs. The proposed method incorporates ensemble discriminators, differentiable augmentation, anti-aliased convolution layers, and a basic 8-layer U-Net generator. This method was trained and tested using a clinical image dataset comprising data of 600 examinations of individuals who underwent dual-energy chest X-ray imaging for diagnostic purposes, using a sixfold cross-validation approach. It demonstrated a remarkable improvement in motion artifact suppression in terms of an analysis of full width at the 10-percent maximum improved from 0.216 ± 0.0720 to 0.200 ± 0.0783 for the left lung region of interests including the cardiac region. Furthermore, it outperformed the method in a previous study in terms of a peak signal-to-noise ratio of 50.7 ± 3.68, structural similarity index of 0.997 ± 0.0152 for LE images, and Fréchet inception distance of 85.0 ± 3.52 for bone-suppressed DES images. The proposed method significantly outperforms existing techniques for removing motion artifacts and statistical noise and shows strong potential for clinical applications in chest X-ray imaging.

Heart disease remains a significant health threat due to its high mortality rate and increasing prevalence. Early prediction using basic physical markers from routine exams is crucial for timely diagnosis and intervention. However, manual analysis of large datasets can be labor-intensive and error-prone. Our goal is to rapidly and reliably anticipate cardiac disease using a variety of body signs. This research presents a unique model for heart disease prediction. We provide a system for predicting cardiac disease that blends the deep convolutional neural network with a feature selection technique based on the LinearSVC. This integrated feature selection method selects a subset of characteristics that are strongly linked with heart disease. We feed these features into the deep conventual neural network that we constructed. Also to improve the speed of the predictor and avoid gradient varnishing or explosion, the network's hyperparameters were tuned using the random search algorithm. The proposed method was evaluated using the UCI and MIT datasets. The predictor is evaluated using a number of indicators, such as accuracy, recall, precision, and F1 score. The results demonstrate that our model attains accuracy rates of 98.16%, 98.2%, 95.38%, and 97.84% in the UCI dataset, with an average MCC score of 90%. These results affirm the efficacy and reliability of the proposed technique to predict heart disease.

Ovarian cancer remains one of the most challenging cancers to diagnose due to its non-specific symptoms, lack of reliable screening tests, and the complexity of detecting abnormalities. Accurate subtype classification is crucial for personalised treatment and improved patient outcomes. In this study, we developed a machine learning pipeline fine-tuning pre-trained computer vision models to classify ovarian cancer subtypes from whole slide images (WSI). Using targeted tissue masks for necrosis, stroma, and tumour regions as a proof of concept, we demonstrated the efficacy of tiling masked regions to transform a complex detection-then-classification problem into a simpler classification task. Our method achieved high accuracy in tile-level classification, with a subsequent extension to subtype classification via majority voting on tiled images. Precision exceeds 90% across subtypes, which highlights the potential of scalable, automated systems to assist in ovarian cancer diagnostics. These findings contribute to the broader field of computational pathology, paving the way for enhanced diagnostic consistency and accessibility in clinical settings.

Chronic kidney disease (CKD) remains a major public health concern, requiring better predictive models for early intervention. This study evaluates a deep learning model (DLM) that utilizes raw chest X-ray (CXR) data to predict moderate to severe kidney function decline. We analyzed data from 79,219 patients with an estimated Glomerular Filtration Rate (eGFR) between 65 and 120, segmented into development (n = 37,983), tuning (n = 15,346), internal validation (n = 14,113), and external validation (n = 11,777) sets. Our DLM, pretrained on CXR-report pairs, was fine-tuned with the development set. We retrospectively examined data spanning April 2011 to February 2022, with a 5-year maximum follow-up. Primary and secondary endpoints included CKD stage 3b progression, ESRD/dialysis, and mortality. The overall concordance index (C-index) values for the internal and external validation sets were 0.903 (95% CI, 0.885-0.922) and 0.851 (95% CI, 0.819-0.883), respectively. In these sets, the incidences of progression to CKD stage 3b at 5 years were 19.2% and 13.4% in the high-risk group, significantly higher than those in the median-risk (5.9% and 5.1%) and low-risk groups (0.9% and 0.9%), respectively. The sex, age, and eGFR-adjusted hazard ratios (HR) for the high-risk group compared to the low-risk group were 16.88 (95% CI, 10.84-26.28) and 7.77 (95% CI, 4.77-12.64), respectively. The high-risk group also exhibited higher probabilities of progressing to ESRD/dialysis or experiencing mortality compared to the low-risk group. Further analysis revealed that the high-risk group compared to the low/median-risk group had a higher prevalence of complications and abnormal blood/urine markers. Our findings demonstrate that a DLM utilizing CXR can effectively predict CKD stage 3b progression, offering a potential tool for early intervention in high-risk populations.

Accurate classification of burn severity is crucial for effective clinical treatment; however, existing methods often fail to balance precision and real-time performance. To address this challenge, we propose a deep learning-based approach utilizing an enhanced ResNet18 architecture with integrated attention mechanisms to improve classification accuracy. The system consists of data preprocessing, classification, optimization, and post-processing modules. The optimization strategy employs an adaptive learning rate combining cosine annealing and class-specific gradient adaptation, alongside targeted adjustments for class imbalance, while an improved Adam optimizer enhances convergence stability. Post-processing incorporates confidence filtering (threshold 0.3) and selective evaluation, with weighted aggregation-integrating dynamic accuracy calculation and moving average to refine predictions and enhance diagnostic reliability. Experimental results on a burn skin test dataset demonstrate that the proposed model achieves a classification accuracy of 99.19% ± 0.12 and a mean average precision (mAP) of 98.72% ± 0.10, highlighting its potential for real-time clinical burn assessment.

Breast ultrasound is a useful and rapid diagnostic tool for the early detection of breast cancer. Artificial intelligence-supported computer-aided decision systems, which assist expert radiologists and clinicians, provide reliable and rapid results. Deep learning methods and techniques are widely used in the field of health for early diagnosis, abnormality detection, and disease diagnosis. Therefore, in this study, a deep ensemble learning model based on Dirichlet distribution using pre-trained transfer learning models for breast cancer classification from ultrasound images is proposed. In the study, experiments were conducted using the Breast Ultrasound Images Dataset (BUSI). The dataset, which had an imbalanced class structure, was balanced using data augmentation techniques. DenseNet201, InceptionV3, VGG16, and ResNet152 models were used for transfer learning with fivefold cross-validation. Statistical analyses, including the ANOVA test and Tukey HSD test, were applied to evaluate the model's performance and ensure the reliability of the results. Additionally, Grad-CAM (Gradient-weighted Class Activation Mapping) was used for explainable AI (XAI), providing visual explanations of the deep learning model's decision-making process. The spaced repetition method, commonly used to improve the success of learners in educational sciences, was adapted to artificial intelligence in this study. The results of training with transfer learning models were used as input for further training, and spaced repetition was applied using previously learned information. The use of the spaced repetition method led to increased model success and reduced learning times. The weights obtained from the trained models were input into an ensemble learning system based on Dirichlet distribution with different variations. The proposed model achieved 99.60% validation accuracy on the dataset, demonstrating its effectiveness in breast cancer classification.