Body composition analysis is vital in assessing health conditions such as obesity, sarcopenia, and metabolic syndromes. MRI provides detailed images of skeletal muscle (SM), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT), but their manual segmentation is labor-intensive and limits clinical applicability. This study validates an automated tool for MRI-based 2D body composition analysis (Data Analysis Facilitation Suite (DAFS) Express), comparing its automated measurements with expert manual segmentations using UK Biobank data. A cohort of 399 participants from the UK Biobank dataset was selected, yielding 423 single L3 slices for analysis. DAFS Express performed automated segmentations of SM, VAT, and SAT, which were then manually corrected by expert raters for validation. Evaluation metrics included Jaccard coefficients, Dice scores, intraclass correlation coefficients (ICCs), and Bland-Altman Plots to assess segmentation agreement and reliability. High agreements were observed between automated and manual segmentations with mean Jaccard scores: SM 99.03%, VAT 95.25%, and SAT 99.57%, and mean Dice scores: SM 99.51%, VAT 97.41%, and SAT 99.78%. Cross-sectional area comparisons showed consistent measurements, with automated methods closely matching manual measurements for SM and SAT, and slightly higher values for VAT (SM: auto 132.51 cm², manual 132.36 cm²; VAT: auto 137.07 cm², manual 134.46 cm²; SAT: auto 203.39 cm², manual 202.85 cm²). ICCs confirmed strong reliability (SM 0.998, VAT 0.994, SAT 0.994). Bland-Altman plots revealed minimal biases, and boxplots illustrated distribution similarities across SM, VAT, and SAT areas. On average, DAFS Express took 18 s per DICOM for a total of 126.9 min for 423 images to output segmentations and measurement PDF's per DICOM. Automated segmentation of SM, VAT, and SAT from 2D MRI images using DAFS Express showed comparable accuracy to manual segmentation. This underscores its potential to streamline image analysis processes in research and clinical settings, enhancing diagnostic accuracy and efficiency. Future work should focus on further validation across diverse clinical applications and imaging conditions.

Accurate assessment of computed tomography (CT) image quality is crucial for ensuring diagnostic accuracy, optimizing imaging protocols, and preventing excessive radiation exposure. In clinical settings, where high-quality reference images are often unavailable, developing no-reference image quality assessment (NR-IQA) methods is essential. Recently, CT-NR-IQA methods using deep learning have been widely studied; however, significant challenges remain in handling multiple degradation factors and accurately reflecting real-world degradations. To address these issues, we propose a novel CT-NR-IQA method. Our approach utilizes a dataset that combines two degradation factors (noise and blur) to train convolutional neural network (CNN) models capable of handling multiple degradation factors. Additionally, we leveraged RadImageNet pre-trained models (ResNet50, DenseNet121, InceptionV3, and InceptionResNetV2), allowing the models to learn deep features from large-scale real clinical images, thus enhancing adaptability to real-world degradations without relying on artificially degraded images. The models' performances were evaluated by measuring the correlation between the subjective scores and predicted image quality scores for both artificially degraded and real clinical image datasets. The results demonstrated positive correlations between the subjective and predicted scores for both datasets. In particular, ResNet50 showed the best performance, with a correlation coefficient of 0.910 for the artificially degraded images and 0.831 for the real clinical images. These findings indicate that the proposed method could serve as a potential surrogate for subjective assessment in CT-NR-IQA.

Diffuse midline glioma (DMG) H3 K27M-altered is a rare pediatric brainstem cancer with poor prognosis. To advance the development of predictive models to gain a deeper understanding of DMG, there is a crucial need for seamlessly integrating automatic and highly accurate tumor segmentation techniques. There is only one method that tries to solve this task in this cancer; for that reason, this study develops a modified CNN-based 3D-Unet tool to automatically segment DMG in an accurate way in magnetic resonance (MR) images. The dataset consisted of 52 DMG patients and 70 images, each with T1W and T2W or FLAIR images. Three different datasets were created: T1W images, T2W or FLAIR images, and a combined set of T1W and T2W/FLAIR images. Denoising, bias field correction, spatial resampling, and normalization were applied as preprocessing steps to the MR images. Patching techniques were also used to enlarge the dataset size. For tumor segmentation, a 3D U-Net architecture with residual blocks was used. The best results were obtained for the dataset composed of all T1W and T2W/FLAIR images, reaching an average Dice Similarity Coefficient (DSC) of 0.883 on the test dataset. These results are comparable to other brain tumor segmentation models and to state-of-the-art results in DMG segmentation using fewer sequences. Our results demonstrate the effectiveness of the proposed 3D U-Net architecture for DMG tumor segmentation. This advancement holds potential for enhancing the precision of diagnostic and predictive models in the context of this challenging pediatric cancer.

This study aimed to develop a fully automated semantic placenta segmentation model that integrates the U-Net and SegNeXt architectures through ensemble learning. A total of 218 pregnant women with suspected placental abnormalities who underwent magnetic resonance imaging (MRI) were enrolled, yielding 1090 annotated images for developing a deep learning model for placental segmentation. The images were standardized and divided into training and test sets. The performance of Placental Segmentation Network (PlaNet-S), which integrates U-Net and SegNeXt within an ensemble framework, was assessed using Intersection over Union (IoU) and counting connected components (CCC) against the U-Net, U-Net + + , and DS-transUNet. PlaNet-S had significantly higher IoU (0.78, SD = 0.10) than that of U-Net (0.73, SD = 0.13) (p < 0.005) and DS-transUNet (0.64, SD = 0.16) (p < 0.005), while the difference with U-Net + + (0.77, SD = 0.12) was not statistically significant. The CCC for PlaNet-S was significantly higher than that for U-Net (p < 0.005), U-Net + + (p < 0.005), and DS-transUNet (p < 0.005), matching the ground truth in 86.0%, 56.7%, 67.9%, and 20.9% of the cases, respectively. PlaNet-S achieved higher IoU than U-Net and DS-transUNet, and comparable IoU to U-Net + + . Moreover, PlaNet-S significantly outperformed all three models in CCC, indicating better agreement with the ground truth. This model addresses the challenges of time-consuming physician-assisted manual segmentation and offers the potential for diverse applications in placental imaging analyses.

Alzheimer's disease (AD) is the most prevalent type of dementia. It is linked with a gradual decline in various brain functions, such as memory. Many research efforts are now directed toward non-invasive procedures for early diagnosis because early detection greatly benefits the patient care and treatment outcome. Additional to an accurate diagnosis and reduction of the rate of misdiagnosis; Computer-Aided Design (CAD) systems are built to give definitive diagnosis. This paper presents a novel CAD system to determine stages of AD. Initially, deep learning techniques are utilized to extract features from the AD brain MRIs. Then, the extracted features are reduced using a proposed feature reduction technique named Deep Downsized Kernel Partial Least Squares (DDKPLS). The proposed approach selects a reduced number of samples from the initial information matrix. The samples chosen give rise to a new data matrix further processed by KPLS to deal with the high dimensionality. The reduced feature space is finally classified using ELM. The implementation is named DDKPLS-ELM. Reference tests have been performed on the Kaggle MRI dataset, which exhibit the efficacy of the DDKPLS-based classifier; it achieves accuracy up to 95.4% and an F1 score of 95.1%.

One possible adverse effect of breast irradiation is the development of pulmonary fibrosis. The aim of this study was to determine whether planning CT scans can predict which patients are more likely to develop lung lesions after treatment. A retrospective analysis of 242 patient records was performed using different machine learning models. These models showed a remarkable correlation between the occurrence of fibrosis and the hounsfield units of lungs in CT data. Three different classification methods (Tree, Kernel-based, k-Nearest Neighbors) showed predictive values above 60%. The human predictive factor (HPF), a mathematical predictive model, further strengthened the association between lung hounsfield unit (HU) metrics and radiation-induced lung injury (RILI). These approaches optimize radiation treatment plans to preserve lung health. Machine learning models and HPF can also provide effective diagnostic and therapeutic support for other diseases.

Early screening, diagnosis, and treatment of lung cancer are pivotal in clinical practice since the tumor stage remains the most dominant factor that affects patient survival. Previous initiatives have tried to develop new tools for decision-making of lung cancer. In this study, we proposed the China Protocol, a complete workflow of lung cancer tailored to the Chinese population, which is implemented by steps including early screening by evaluation of risk factors and three-dimensional thin-layer image reconstruction technique for low-dose computed tomography (Tre-LDCT), accurate diagnosis via artificial intelligence (AI) and novel biomarkers, and individualized treatment through non-invasive molecule visualization strategies. The application of this protocol has improved the early diagnosis and 5-year survival rates of lung cancer in China. The proportion of early-stage (stage I) lung cancer has increased from 46.3% to 65.6%, along with a 5-year survival rate of 90.4%. Moreover, especially for stage IA1 lung cancer, the diagnosis rate has improved from 16% to 27.9%; meanwhile, the 5-year survival rate of this group achieved 97.5%. Thus, here we defined stage IA1 lung cancer, which cohort benefits significantly from early diagnosis and treatment, as the "ultra-early stage lung cancer", aiming to provide an intuitive description for more precise management and survival improvement. In the future, we will promote our findings to multicenter remote areas through medical alliances and mobile health services with the desire to move forward the diagnosis and treatment of lung cancer.

In recent years, scientists have been trying to convert magnetic resonance imaging (MRI) images into computed tomography (CT) images for dose calculations while taking advantage of the benefits of MRI images. The main approaches for image conversion are bulk density, Atlas registration, and machine learning. These methods have limitations in accuracy and time consumption and require large datasets to convert images. In this study, the novel 'voxels spawn voxels' technique combined with the 'orthonormalize' feature in Carimas software was developed to build a conversion dataset from MRI intensity to Hounsfield unit value for some structural regions including gluteus maximus, liver, kidneys, spleen, pancreas, and colon. The original CT images and the converted MRI images were imported into the Geant4/Gamos software for dose calculation. It gives good results (<5%) in most organs except the intestine (18%).

The automated generation of radiology reports from chest X-ray images holds significant promise in enhancing diagnostic workflows while preserving patient privacy. Traditional centralized approaches often require sensitive data transfer, posing privacy concerns. To address this, the study proposes a Multimodal Federated Learning framework for chest X-ray report generation using the IU-Xray dataset. The system utilizes a Vision Transformer (ViT) as the encoder and GPT-2 as the report generator, enabling decentralized training without sharing raw data. Three Federated Learning (FL) aggregation strategies: FedAvg, Krum Aggregation and a novel Loss-aware Federated Averaging (L-FedAvg) were evaluated. Among these, Krum Aggregation demonstrated superior performance across lexical and semantic evaluation metrics such as ROUGE, BLEU, BERTScore and RaTEScore. The results show that FL can match or surpass centralized models in generating clinically relevant and semantically rich radiology reports. This lightweight and privacy-preserving framework paves the way for collaborative medical AI development without compromising data confidentiality.

Predicting the risk of developing breast cancer is an important clinical tool to guide early intervention and tailoring personalized screening strategies. Early risk models have limited performance and recently machine learning-based analysis of mammogram images showed encouraging risk prediction effects. These models however are limited to the use of a single exam or tend to overlook nuanced breast tissue evolvement in spatial and temporal details of longitudinal imaging exams that are indicative of breast cancer risk. In this paper, we propose STA-Risk (Spatial and Temporal Asymmetry-based Risk Prediction), a novel Transformer-based model that captures fine-grained mammographic imaging evolution simultaneously from bilateral and longitudinal asymmetries for breast cancer risk prediction. STA-Risk is innovative by the side encoding and temporal encoding to learn spatial-temporal asymmetries, regulated by a customized asymmetry loss. We performed extensive experiments with two independent mammogram datasets and achieved superior performance than four representative SOTA models for 1- to 5-year future risk prediction. Source codes will be released upon publishing of the paper.