Deep learning-based myocardial scar segmentation from late gadolinium enhancement (LGE) cardiac MRI has shown great potential for accurate and timely diagnosis and treatment planning for structural cardiac diseases. However, the limited availability and variability of LGE images with high-quality scar labels restrict the development of robust segmentation models. To address this, we introduce CLAIM: \textbf{C}linically-Guided \textbf{L}GE \textbf{A}ugmentation for Real\textbf{i}stic and Diverse \textbf{M}yocardial Scar Synthesis and Segmentation framework, a framework for anatomically grounded scar generation and segmentation. At its core is the SMILE module (Scar Mask generation guided by cLinical knowledgE), which conditions a diffusion-based generator on the clinically adopted AHA 17-segment model to synthesize images with anatomically consistent and spatially diverse scar patterns. In addition, CLAIM employs a joint training strategy in which the scar segmentation network is optimized alongside the generator, aiming to enhance both the realism of synthesized scars and the accuracy of the scar segmentation performance. Experimental results show that CLAIM produces anatomically coherent scar patterns and achieves higher Dice similarity with real scar distributions compared to baseline models. Our approach enables controllable and realistic myocardial scar synthesis and has demonstrated utility for downstream medical imaging task.

This paper presents a lightweight framework for classifying brain stroke types from Diffusion-Weighted Imaging (DWI) MRI scans, employing a Multi-Layer Perceptron (MLP) neural network with Wavelet Transform for feature extraction. Accurate and timely stroke detection is critical for effective treatment and improved patient outcomes in neuroimaging. While Convolutional Neural Networks (CNNs) are widely used for medical image analysis, their computational complexity often hinders deployment in resource-constrained clinical settings. In contrast, our approach combines Wavelet Transform with a compact MLP to achieve efficient and accurate stroke classification. Using the "Brain Stroke MRI Images" dataset, our method yields classification accuracies of 82.0% with the "db4" wavelet (level 3 decomposition) and 86.00% with the "Haar" wavelet (level 2 decomposition). This analysis highlights a balance between diagnostic accuracy and computational efficiency, offering a practical solution for automated stroke diagnosis. Future research will focus on enhancing model robustness and integrating additional MRI modalities for comprehensive stroke assessment.

Multi-parametric magnetic resonance imaging (mpMRI) exams have various series types acquired with different imaging protocols. The DICOM headers of these series often have incorrect information due to the sheer diversity of protocols and occasional technologist errors. To address this, we present a deep learning-based classification model to classify 8 different body mpMRI series types so that radiologists read the exams efficiently. Using mpMRI data from various institutions, multiple deep learning-based classifiers of ResNet, EfficientNet, and DenseNet are trained to classify 8 different MRI series, and their performance is compared. Then, the best-performing classifier is identified, and its classification capability under the setting of different training data quantities is studied. Also, the model is evaluated on the out-of-training-distribution datasets. Moreover, the model is trained using mpMRI exams obtained from different scanners in two training strategies, and its performance is tested. Experimental results show that the DenseNet-121 model achieves the highest F1-score and accuracy of 0.966 and 0.972 over the other classification models with p-value$<$0.05. The model shows greater than 0.95 accuracy when trained with over 729 studies of the training data, whose performance improves as the training data quantities grew larger. On the external data with the DLDS and CPTAC-UCEC datasets, the model yields 0.872 and 0.810 accuracy for each. These results indicate that in both the internal and external datasets, the DenseNet-121 model attains high accuracy for the task of classifying 8 body MRI series types.

Brain tumor classification using Magnetic Resonance Imaging (MRI) images is an important and emerging field of medical imaging and artificial intelligence in the current world. With advancements in technology, particularly in deep learning and machine learning, researchers and clinicians are leveraging these tools to create complex models that, using MRI data, can reliably detect and classify tumors in the brain. However, it has a number of drawbacks, including the intricacy of tumor types and grades, intensity variations in MRI data and tumors varying in severity. This paper proposes a Multi-Grade Hierarchical Classification Network Model (MGHCN) for the hierarchical classification of tumor grades in MRI images. The model's distinctive feature lies in its ability to categorize tumors into multiple grades, thereby capturing the hierarchical nature of tumor severity. To address variations in intensity levels across different MRI samples, an Improved Adaptive Intensity Normalization (IAIN) pre-processing step is employed. This step standardizes intensity values, effectively mitigating the impact of intensity variations and ensuring more consistent analyses. The model renders utilization of the Dual Tree Complex Wavelet Transform with Enhanced Trigonometric Features (DTCWT-ETF) for efficient feature extraction. DTCWT-ETF captures both spatial and frequency characteristics, allowing the model to distinguish between different tumor types more effectively. In the classification stage, the framework introduces the Adaptive Hierarchical Optimized Horse Herd BiLSTM Fusion Network (AHOHH-BiLSTM). This multi-grade classification model is designed with a comprehensive architecture, including distinct layers that enhance the learning process and adaptively refine parameters. The purpose of this study is to improve the precision of distinguishing different grades of tumors in MRI images. To evaluate the proposed MGHCN framework, a set of evaluation metrics is incorporated which includes precision, recall, and the F1-score. The structure employs BraTS Challenge 2021, Br35H, and BraTS Challenge 2023 datasets, a significant combination that ensures comprehensive training and evaluation. The MGHCN framework aims to enhance brain tumor classification in MRI images by utilizing these datasets along with a comprehensive set of evaluation metrics, providing a more thorough and sophisticated understanding of its capabilities and performance.

Prostate cancer (PCa) is one of the most common types of cancer, seriously affecting adult male health. Accurate and automated PCa segmentation is essential for radiologists to confirm the location of cancer, evaluate its severity, and design appropriate treatments. This paper presents PCaSAM, a fully automated PCa segmentation model that allows us to input multi-modal MRI images into the foundation model to improve performance significantly. We collected multi-center datasets to conduct a comprehensive evaluation. The results showed that PCaSAM outperforms the generalist medical foundation model and the other representative segmentation models, with the average DSC of 0.721 and 0.706 in the internal and external datasets, respectively. Furthermore, with the assistance of segmentation, the PI-RADS scoring of PCa lesions was improved significantly, leading to a substantial increase in average AUC by 8.3-8.9% on two external datasets. Besides, PCaSAM achieved superior efficiency, making it highly suitable for real-world deployment scenarios.

Alzheimer's disease (AD) is a leading cause of death, making early detection critical to improve survival rates. Conventional manual techniques struggle with early diagnosis due to the brain's complex structure, necessitating the use of dependable deep learning (DL) methods. This research proposes a novel RESIGN model is a combination of Res-InceptionSeg for detecting AD utilizing MRI images. The input MRI images were pre-processed using a Non-Local Means (NLM) filter to reduce noise artifacts. A ResNet-LSTM model was used for feature extraction, targeting White Matter (WM), Grey Matter (GM), and Cerebrospinal Fluid (CSF). The extracted features were concatenated and classified into Normal, MCI, and AD categories using an Inception V3-based classifier. Additionally, SegNet was employed for abnormal brain region segmentation. The RESIGN model achieved an accuracy of 99.46%, specificity of 98.68%, precision of 95.63%, recall of 97.10%, and an F1 score of 95.42%. It outperformed ResNet, AlexNet, Dense- Net, and LSTM by 7.87%, 5.65%, 3.92%, and 1.53%, respectively, and further improved accuracy by 25.69%, 5.29%, 2.03%, and 1.71% over ResNet18, CLSTM, VGG19, and CNN, respectively. The integration of spatial-temporal feature extraction, hybrid classification, and deep segmentation makes RESIGN highly reliable in detecting AD. A 5-fold cross-validation proved its robustness, and its performance exceeded that of existing models on the ADNI dataset. However, there are potential limitations related to dataset bias and limited generalizability due to uniform imaging conditions. The proposed RESIGN model demonstrates significant improvement in early AD detection through robust feature extraction and classification by offering a reliable tool for clinical diagnosis.

In functional MRI, a hemodynamic response function (HRF) describes how neural events are translated into a blood oxygenation response detected through imaging. The HRF has the potential to quantify neurovascular mechanisms by which cardiovascular risks modify brain health, but relationships among HRF characteristics, brain health, and cardiovascular modifiers of brain health have not been well studied to date. One hundred and thirty-seven middle-aged participants (mean age: 53.6±4.7, female (62%), 78% White American participants and 22% African American participants) in the exploratory analysis from Bogalusa Heart Study completed clinical evaluations from childhood to midlife and an adaptive Stroop task during fMRI in midlife. The HRFs of each participant within seventeen brain regions of interest (ROIs) previously identified as activated by this task were calculated using a convolutional neural network approach. Faster and more efficient neurovascular functioning was characterized in terms of five HRF characteristics: faster time to peak (TTP), shorter full width at half maximum (FWHM), smaller peak magnitude (PM), smaller trough magnitude (TM), and smaller area under the HRF curve (AUHRF). The composite HRF summary characteristics over all ROIs were calculated for multivariable and simple linear regression analyses. In multivariable models, faster and more efficient HRF characteristic was found in non-smoker compared to smokers (AUHRF, p = 0.029). Faster and more efficient HRF characteristics were associated with lower systolic and diastolic blood pressures (FWHM, TM, and AUHRF, p = 0.030, 0.042, and 0.032) and cerebral amyloid burden (FWHM, p-value = 0.027) in midlife; as well as greater response rate on the Stroop task (FWHM, p = 0.042) in midlife. In simple linear regression models, faster and more efficient HRF characteristics were found in women compared to men (TM, p = 0.019); in White American participants compared to African American participants (AUHRF, p = 0.044); and in non-smokers compared to smokers (TTP and AUHRF, p = 0.019 and 0.010). Faster and more efficient HRF characteristics were associated with lower systolic and diastolic blood pressures (FWHM and TM, p = 0.019 and 0.029), and lower BMI (FWHM and AUHRF, p = 0.025 and 0.017), in childhood and adolescence; and lower BMI (TTP, p = 0.049), cerebral amyloid burden (FWHM, p = 0.002), and white matter hyperintensity burden (FWHM, p = 0.046) in midlife; as well as greater accuracy on the Stroop task (AUHRF, p = 0.037) in midlife. In a diverse middle-aged community sample, HRF-based indicators of faster and more efficient neurovascular functioning were associated with better brain health and cognitive function, as well as better lifespan cardiovascular health.

Deformable image registration is a fundamental task in medical image analysis, which is crucial in enabling early detection and accurate disease diagnosis. Although transformer-based architectures have demonstrated strong potential through attention mechanisms, challenges remain in ineffective feature extraction and spatial alignment, particularly within hierarchical attention frameworks. To address these limitations, we propose a novel registration framework that integrates hierarchical feature encoding in the encoder and an adaptive cascaded refinement strategy in the decoder. The model employs hierarchical cross-attention between fixed and moving images at multiple scales, enabling more precise alignment and improved registration accuracy. The decoder incorporates the Adaptive Cascaded Module (ACM), facilitating progressive deformation field refinement across multiple resolution levels. This approach captures coarse global transformations and acceptable local variations, resulting in smooth and anatomically consistent alignment. However, rather than relying solely on the final decoder output, our framework leverages intermediate representations at each stage of the network, enhancing the robustness and precision of the registration process. Our method achieves superior accuracy and adaptability by integrating deformations across all scales. Comprehensive experiments on two widely used 3D brain MRI datasets, OASIS and LPBA40, demonstrate that the proposed framework consistently outperforms existing state-of-the-art approaches across multiple evaluation metrics regarding accuracy, robustness, and generalizability.

$\textbf{Objective:}$ Brain-predicted age difference (BrainAGE) is a neuroimaging biomarker reflecting brain health. However, training robust BrainAGE models requires large datasets, often restricted by privacy concerns. This study evaluates the performance of federated learning (FL) for BrainAGE estimation in ischemic stroke patients treated with mechanical thrombectomy, and investigates its association with clinical phenotypes and functional outcomes. $\textbf{Methods:}$ We used FLAIR brain images from 1674 stroke patients across 16 hospital centers. We implemented standard machine learning and deep learning models for BrainAGE estimates under three data management strategies: centralized learning (pooled data), FL (local training at each site), and single-site learning. We reported prediction errors and examined associations between BrainAGE and vascular risk factors (e.g., diabetes mellitus, hypertension, smoking), as well as functional outcomes at three months post-stroke. Logistic regression evaluated BrainAGE's predictive value for these outcomes, adjusting for age, sex, vascular risk factors, stroke severity, time between MRI and arterial puncture, prior intravenous thrombolysis, and recanalisation outcome. $\textbf{Results:}$ While centralized learning yielded the most accurate predictions, FL consistently outperformed single-site models. BrainAGE was significantly higher in patients with diabetes mellitus across all models. Comparisons between patients with good and poor functional outcomes, and multivariate predictions of these outcomes showed the significance of the association between BrainAGE and post-stroke recovery. $\textbf{Conclusion:}$ FL enables accurate age predictions without data centralization. The strong association between BrainAGE, vascular risk factors, and post-stroke recovery highlights its potential for prognostic modeling in stroke care.

Objective: Our study aimed to evaluate and validate PanSegNet, a deep learning (DL) algorithm for pediatric pancreas segmentation on MRI in children with acute pancreatitis (AP), chronic pancreatitis (CP), and healthy controls. Methods: With IRB approval, we retrospectively collected 84 MRI scans (1.5T/3T Siemens Aera/Verio) from children aged 2-19 years at Gazi University (2015-2024). The dataset includes healthy children as well as patients diagnosed with AP or CP based on clinical criteria. Pediatric and general radiologists manually segmented the pancreas, then confirmed by a senior pediatric radiologist. PanSegNet-generated segmentations were assessed using Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff distance (HD95). Cohen's kappa measured observer agreement. Results: Pancreas MRI T2W scans were obtained from 42 children with AP/CP (mean age: 11.73 +/- 3.9 years) and 42 healthy children (mean age: 11.19 +/- 4.88 years). PanSegNet achieved DSC scores of 88% (controls), 81% (AP), and 80% (CP), with HD95 values of 3.98 mm (controls), 9.85 mm (AP), and 15.67 mm (CP). Inter-observer kappa was 0.86 (controls), 0.82 (pancreatitis), and intra-observer agreement reached 0.88 and 0.81. Strong agreement was observed between automated and manual volumes (R^2 = 0.85 in controls, 0.77 in diseased), demonstrating clinical reliability. Conclusion: PanSegNet represents the first validated deep learning solution for pancreatic MRI segmentation, achieving expert-level performance across healthy and diseased states. This tool, algorithm, along with our annotated dataset, are freely available on GitHub and OSF, advancing accessible, radiation-free pediatric pancreatic imaging and fostering collaborative research in this underserved domain.